Methods and systems for processing data via an executable file on a monitor to reduce the dimensionality of the data and encrypting the data being transmitted over the wireless

ABSTRACT

Some embodiments include processing data via an executable file on a monitor to reduce the dimensionality of the data being transmitted over the wireless network. The output of the executable file also encrypts the data before being transmitted wireless to a remote server. The remote server receives the transmitted data and makes likelihood inferences based on the recorded data.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority from U.S. patent application Ser. No.17/651,773, filed Feb. 18, 2022, which claims priority from U.S. patentapplication Ser. No. 17/397,075, filed Aug. 9, 2021, which claimspriority from U.S. patent application Ser. No. 17/174,145, filed Feb.11, 2021, which claims priority from provisional U.S. Pat. App. No.62/975,626, filed on Feb. 12, 2020 and from provisional U.S. Pat. App.No. 63/090,951, filed on Oct. 13, 2020, which are hereby incorporated byreference in their entirety.

BACKGROUND

For purposes of this disclosure, certain aspects, advantages, and novelfeatures of various embodiments are described herein. It is to beunderstood that not necessarily all such advantages may be achieved inaccordance with any particular embodiment. Thus, various embodiments maybe or carried out in a manner that achieves one advantage or group ofadvantages as taught herein without necessarily achieving otheradvantages as may be taught or suggested herein.

BRIEF SUMMARY OF EMBODIMENTS

Embodiments described herein are directed to a physiological monitoringdevice that may be worn continuously and comfortably by a human oranimal subject for at least one week or more and more typically two tothree weeks or more. In one embodiment, the device is specificallydesigned to sense and record cardiac rhythm (for example,electrocardiogram, ECG) data, although in various alternativeembodiments one or more additional physiological parameters may besensed and recorded. Such physiological monitoring devices may include anumber of features to facilitate and/or enhance the patient experienceand to make diagnosis of cardiac arrhythmias more accurate and timely.

Some embodiments include a wearable cardiac monitor device formonitoring cardiac rhythm signal data of a user, the wearable cardiacmonitor device comprising: a watertight housing; a surface on thehousing configured to be sealably engaged to a mammal; an adhesive onthe surface configured to remain continuously affixed to the mammal forat least 7 days, without removal until completion of monitoring; atleast two electrodes permanently disposed within the housing, positionedto detect continuous cardiac rhythm signals of the mammal while thesurface is sealably engaged to the mammal; a hardware processorconfigured to process the detected cardiac rhythm signals through afirst subset of computer-executable instructions; and a transmitter, thetransmitter configured to transmit the output of the first subset to acomputing system, the computing system configured to infer a likelihoodof an occurrence of cardiac arrhythmia by processing the output througha second subset of computer-executable instructions.

In some embodiments, the arrhythmia comprises at least one of:ventricular tachycardia, supraventricular tachycardia, ectopy,ventricular fibrillation, or extended pauses.

In some embodiments, to infer a likelihood of an occurrence of cardiacarrhythmia comprises processing the output of the first subset throughthe second subset, wherein the first subset processed at least 24 hoursof continuously detected, stored cardiac rhythm signals.

In some embodiments, the housing comprises a patient trigger configuredto depress and initiate recordation of an instance in time of aperceived cardiac event.

In some embodiments, the electrodes are disposed entirely within thehousing.

In some embodiments, the first subset of computer-executableinstructions comprise a first subset of layers of a neural network,wherein the second subset of computer-executable instructions comprise asecond subset of layers of the neural network.

Some embodiments include a wearable cardiac monitor device formonitoring bio-signal data of a user, the wearable device comprising: anadhesive assembly comprising a housing and a wing, the wing comprisingan electrode configured to detect cardiac signals from a user; ahardware processor configured to process the detected cardiac signalsthrough a first subset of layers of a neural network; and a transmitter,the transmitter configured to transmit the output of the first subset toa computing system, the computing system configured to infer alikelihood of an occurrence of cardiac arrhythmia by processing theoutput through a second subset of layers of the neural network.

In some embodiments, the computing system is further configured todetermine an atrial fibrillation burden from the detected cardiacsignals.

In some embodiments, the atrial fibrillation burden comprises an amountof time spent in atrial fibrillation by the user during a period oftime.

In some embodiments, the atrial fibrillation burden comprises an amountof time spent in atrial fibrillation by the user during a sleep periodand during a wake period.

Some embodiments include a wearable device for monitoring bio-signaldata of a user, the wearable device comprising: an assembly comprisingan electrode configured to detect cardiac signals from a user; ahardware processor configured to process the detected cardiac signalsthrough a first subset of layers of a neural network; and a transmitter,the transmitter configured to transmit the output of the first subset toa computing system, the computing system configured to infer alikelihood of an occurrence of cardiac arrhythmia by processing theoutput through a second subset of layers of the neural network.

In some embodiments, the hardware processor is configured to process thedetected cardiac signals through the first subset of layers of theneural network continuously in substantially real-time of detecting ECGsignals.

In some embodiments, the output of the first subset of layers of theneural network comprises an indication of an R peak.

In some embodiments, the computing system is further configured toreconstruct the cardiac signals via the second subset of layers.

In some embodiments, the output of the first subset of layers of theneural network is of a smaller dimensionality than the input of thefirst subset of layers of the neural network.

In some embodiments, the hardware processor is configured to select theneural network from a plurality of neural networks based on acharacteristic.

In some embodiments, the characteristic comprises at least one of: acharacteristic of the wearable device, a network characteristic betweenthe wearable device and the computing system.

In some embodiments, the hardware processor is further configured tocompress the output of the first subset of layers of the neural network,wherein to transmit the output of the first subset to the computingsystem comprises transmitting the compressed output, wherein thecomputing system is configured to decompress the transmitted data; andwherein processing the output through the second subset of layerscomprises processing the decompressed data.

In some embodiments, the hardware processor is further configured toquantize the output of the first subset of layers of the neural network,wherein to transmit the output of the first subset to the computingsystem comprises transmitting the output.

In some embodiments, the hardware processor is further configured tocompress the quantized output, wherein to transmit the output of thefirst subset to the computing system comprises transmitting thecompressed output, wherein the computing system is configured todecompress the transmitted data; and wherein processing the outputthrough the second subset of layers comprises processing thedecompressed data.

In some embodiments, to transmit the output of the first subset to thecomputing system comprises transmitting the quantized output.

Some embodiments include a monitor to infer a likelihood of acharacteristic of a user, the monitor comprising: a housing; at leasttwo electrodes permanently disposed within the housing, positioned todetect continuous signals of a surface; a printed circuit board assemblycomprising a hardware processor configured to process the detectedsignals through a first subset of computer executable instructions; anda transmitter, the transmitter configured to transmit the data output ofthe first subset of computer executable instructions to a computingsystem, the computing system configured to infer a likelihood of anoccurrence by processing the data output through a second subset ofcomputer executable instructions.

In some embodiments, the dimensionality of the data output of the firstsubset of the computer executable instructions that is transmitted tothe computing system is smaller than the data of the detected signalsfrom the at least two electrodes.

In some embodiments, the data output of the first subset of the computerexecutable instructions is encrypted, wherein the transmitter transmitsthe encrypted data output, wherein the computing system processes theencrypted data output through the second subset of computer executableinstructions.

In some embodiments, the transmitter transmits the data output to thecomputing system via a wireless communication channel.

In some embodiments, the monitor further comprises a receiver configuredto receive an updated first subset of computer executable instructionsfrom the computing system and updating the first subset of the computerexecutable instructions to the updated first subset of computerexecutable instructions, wherein the hardware processor is furtherconfigured to process signals through the updated first subset ofcomputer executable instructions.

In some embodiments, the monitor further comprises an accelerometerconfigured to gather motion data, wherein the computing system isconfigured to match motion data with the detected signals to infer thelikelihood of the occurrence.

In some embodiments, the monitor is a cardiac monitor, wherein thecontinuous signals are cardiac signals, wherein the occurrence is anarrhythmia event.

In some embodiments, the first subset of computer executableinstructions comprise a first subset of layers of a neural network, andthe second subset of computer executable instructions comprise a secondsubset of layers of the neural network.

Some embodiments include a monitor to infer a likelihood of acharacteristic of a user, the monitor comprising: a watertight housing;a surface on the housing configured to be sealably engaged to a mammal;an adhesive on the surface configured to remain continuously affixed tothe mammal for at least 7 days, without removal until completion ofmonitoring; at least two electrodes permanently disposed within thehousing, positioned to detect continuous cardiac rhythm signals of themammal while the surface is sealably engaged to the mammal; a printedcircuit board assembly comprising a hardware processor configured toprocess the detected cardiac rhythm signals through a first subset ofcomputer executable instructions; and a transmitter, the transmitterconfigured to transmit the data output of the first subset of computerexecutable instructions to a computing system, the computing systemconfigured to infer a likelihood of an occurrence of arrhythmia byprocessing the data output through a second subset of computerexecutable instructions.

In some embodiments, the arrhythmia comprises at least one of:ventricular tachycardia, supraventricular tachycardia, ectopy,ventricular fibrillation, or extended pauses.

In some embodiments, to infer a likelihood of an occurrence of cardiacarrhythmia comprises processing the output of the first subset throughthe second subset, wherein the first subset processed at least 24 hoursof continuously detected, stored cardiac rhythm signals.

In some embodiments, the housing comprises a patient trigger configuredto depress and initiate recordation of an instance in time of aperceived cardiac event.

Some embodiments include a system for training a neural network to infera likelihood of a characteristic of a user, the system comprising: awearable device comprising: an assembly comprising an electrodeconfigured to detect cardiac signals from a user; a hardware processorconfigured to process the detected cardiac signals through a firstsubset of layers of a neural network; and a transmitter, the transmitterconfigured to transmit the output of the first subset to a computingsystem, the computing system configured to infer a likelihood of anoccurrence of cardiac arrhythmia by processing the output through asecond subset of layers of the neural network, wherein the system isconfigured to train the neural network by: training a first neuralnetwork to identify a first feature by processing first training data ofa first time period through the first neural network; freezing weightsof the first neural network; training a second neural network toidentify a second feature by processing second training data of a secondtime period through the first and second neural network, wherein thesecond time period is longer than the first time period; unfreezingweights of the first neural network; and training the first and secondneural network simultaneously to identify the second feature byprocessing third training data of a third time period through the firstand second neural network, wherein the third time period is longer thanthe first time period.

In some embodiments, the output of the first subset of layers of theneural network is of a smaller dimensionality than the input of thefirst subset of layers of the neural network.

In some embodiments, the hardware processor is configured to select theneural network from a plurality of neural networks based on acharacteristic.

In some embodiments, the characteristic comprises at least one of: acharacteristic of the wearable device, a network characteristic betweenthe wearable device and the computing system.

In some embodiments, the hardware processor is further configured tocompress the output of the first subset of layers of the neural network,wherein to transmit the output of the first subset to the computingsystem comprises transmitting the compressed output, wherein thecomputing system is configured to decompress the transmitted data; andwherein processing the output through the second subset of layerscomprises processing the decompressed data.

In some embodiments, the hardware processor is further configured toquantize the output of the first subset of layers of the neural network,wherein to transmit the output of the first subset to the computingsystem comprises transmitting the output.

Some embodiments include a method for training a neural network to infera likelihood of a characteristic of a user, the method comprising:training a first neural network to identify a first feature byprocessing first training data of a first time period through the firstneural network; freezing weights of the first neural network; training asecond neural network to identify a second feature by processing secondtraining data of a second time period through the first and secondneural network, wherein the second time period is longer than the firsttime period; unfreezing weights of the first neural network; andtraining the first and second neural network simultaneously to identifythe second feature by processing third training data of a third timeperiod through the first and second neural network, wherein the thirdtime period is longer than the first time period.

In some embodiments, the second training data is the third trainingdata, and the second time period is the third time period.

In some embodiments, training is based on available processing power ofa wearable device.

In some embodiments, training is based on available memory of a wearabledevice.

In some embodiments, training is based on network availability between awearable device and an external computing system.

In some embodiments, training is based on a type of the characteristicof the user.

In some embodiments, the type of the characteristic of the user includesan occurrence of cardiac arrhythmia.

In certain embodiments, the computing system is further configured toprovide a report, the report comprising the likelihood of the occurrenceof cardiac arrhythmia.

In certain embodiments, the report comprises a graph over time of atrialfibrillation burden.

In certain embodiments, the report comprises indications for a presenceof atrial fibrillation.

In certain embodiments, the report comprises at least a 14 daymonitoring period.

In certain embodiments, the computing system is a server or a gateway.

In certain embodiments, the computing system is a smartphone.

In certain embodiments, the computing system communicates with thetransmitter through a smartphone intermediary.

In certain embodiments, the wearable device is configured to collect asecondary signal and the hardware processor is configured to process thesecondary signal through the first subset of layers of the neuralnetwork.

In certain embodiments, the wearable device further comprises anaccelerometer configured to measure movement of the user, and whereinthe secondary signal is accelerometer data.

In certain embodiments, the computing system is further configured todetermine an atrial fibrillation burden, and the atrial fibrillationburden comprises an amount of time spent in atrial fibrillation duringmovement of the user.

In certain embodiments, the movement of the user comprises a firstdegree of movement and a second degree of movement.

In certain embodiments, the secondary signal is electrode contactquality data.

In certain embodiments, the electrodes and hardware processor arecontained within a chest strap.

In certain embodiments, the electrodes and hardware processor arecontained within a watch, configured to be worn on a human wrist.

In certain embodiments, the electrodes and hardware processor arecontained within a wearable fitness band.

In certain embodiments, the arrhythmia comprises ventriculartachycardia.

In certain embodiments, the arrhythmia comprises supraventriculartachycardia.

In certain embodiments, the arrhythmia comprises ectopy.

In certain embodiments, the arrhythmia comprises ventricularfibrillation.

In certain embodiments, the arrhythmia comprises extended pauses.

In certain embodiments, the hardware processor is configured to beremoved from the wearable device and modified while removed from thewearable device.

In certain embodiments, to quantize the output comprises rounding ortruncating the values in the output.

In certain embodiments, the amount of quantization to the output isbased on lossless compression performance.

In certain embodiments, the amount of quantization to the output isbased on an efficiency on at least one of: processing power, storage, ornetwork usage, and accuracy of the neural network.

In certain embodiments, the neural network is trained by applyingquantization to the output of the first subset of layers of the neuralnetwork.

In certain embodiments, the neural network is trained by applyinglossless compression to the output of the first subset of layers of theneural network.

In certain embodiments, the neural network is trained by applyingquantization and lossless compression to the output of the first subsetof layers of the neural network.

In certain embodiments, the neural network is trained withoutquantization and lossless compression to generate a first neuralnetwork, and the first neural network is trained by applyingquantization and lossless compression to the output of the first subsetof layers of the neural network to generate a second neural network.

Some embodiments include a wearable device for monitoring data, thewearable device comprising: a sensor configured to detect cardiacsignals from a user; a hardware processor configured to process thedetected cardiac signals through a first subset of layers of a neuralnetwork; and a transmitter, the transmitter configured to transmit theoutput of the first subset to a computing system, the computing systemconfigured to infer a likelihood of an occurrence of cardiac arrhythmiaby processing the output through a second subset of layers of the neuralnetwork.

Some embodiments include a computing system for estimating a likelihoodof an occurrence of cardiac arrhythmia, the computing system comprising:one or more first hardware processors configured to: receive data from awearable device, wherein the wearable device comprises a sensorconfigured to detect cardiac signals from a user, and one or more secondhardware processors configured to process the detected cardiac signalsthrough a first subset of layers of a neural network; process thereceived data through a second subset of layers of the neural network;and receive, from the output of the second subset of layers, aninference of a likelihood of an occurrence of cardiac arrhythmia.

Some embodiments include a method for monitoring data, the methodcomprising: detecting cardiac signals from a user; processing thedetected cardiac signals through a first subset of layers of a neuralnetwork; and transmitting the output of the first subset to a computingsystem, the computing system inferring a likelihood of an occurrence ofcardiac arrhythmia by processing the output through a second subset oflayers of the neural network.

Some embodiments include a non-transitory computer storage mediumstoring computer-executable instructions that, when executed by aprocessor, cause the processor to perform the following method:detecting cardiac signals from a user; processing the detected cardiacsignals through a first subset of layers of a neural network; andtransmitting the output of the first subset to a computing system, thecomputing system inferring a likelihood of an occurrence of cardiacarrhythmia by processing the output through a second subset of layers ofthe neural network.

Some embodiments include a method for training a neural network to infera likelihood of a characteristic of a user, the method comprising:training a first neural network to identify a first feature byprocessing first training data of a first time period through the firstneural network; freezing weights of the first neural network; training asecond neural network to identify a second feature by processing secondtraining data of a second time period through the first and secondneural network, wherein the second time period is longer than the firsttime period; unfreezing weights of the first neural network; andtraining the first and second neural network simultaneously to identifythe second feature by processing third training data of a third timeperiod through the first and second neural network, wherein the thirdtime period is longer than the first time period.

In certain embodiments, the second training data is the third trainingdata, and the second time period is the third time period.

In certain embodiments, training is based on available processing powerof a wearable device.

In certain embodiments, training is based on available memory of awearable device.

In certain embodiments, training is based on network availabilitybetween a wearable device and an external computing system.

In certain embodiments, training is based on a type of thecharacteristic of the user.

In certain embodiments, the type of the characteristic of the userincludes an occurrence of cardiac arrhythmia.

Some embodiments include a system for training a neural network to infera likelihood of a characteristic of a user, the system comprising: awearable cardiac monitor device comprising: a watertight housing; asurface on the housing configured to be sealably engaged to a mammal; anadhesive on the surface configured to remain continuously affixed to themammal for at least 7 days, without removal until completion ofmonitoring; at least two electrodes permanently disposed within thehousing, positioned to detect continuous cardiac rhythm signals of themammal while the surface is sealably engaged to the mammal; a hardwareprocessor configured to process the detected cardiac rhythm signalsthrough a first subset of layers of a neural network; and a transmitter,the transmitter configured to transmit the output of the first subset toa computing system, the computing system configured to infer alikelihood of an occurrence of cardiac arrhythmia by processing theoutput through a second subset of layers of the neural network, whereinthe system is configured to train the neural network by: training afirst neural network to identify a first feature by processing firsttraining data of a first time period through the first neural network;freezing weights of the first neural network; training a second neuralnetwork to identify a second feature by processing second training dataof a second time period through the first and second neural network,wherein the second time period is longer than the first time period;unfreezing weights of the first neural network; and training the firstand second neural network simultaneously to identify the second featureby processing third training data of a third time period through thefirst and second neural network, wherein the third time period is longerthan the first time period.

Some embodiments include a wearable device for monitoring bio-signaldata of a user, the wearable device comprising: an adhesive assemblycomprising a housing and a wing, the wing comprising an electrodeconfigured to detect cardiac signals from a user; a hardware processorconfigured to process the detected cardiac signals through a firstsubset of layers of a neural network; and a transmitter, the transmitterconfigured to transmit the output of the first subset to a computingsystem, the computing system configured to infer a QT interval byprocessing the output through a second subset of layers of the neuralnetwork.

In certain embodiments, the computing system is further configured todetermine an atrial fibrillation burden from the detected cardiacsignals.

In certain embodiments, the atrial fibrillation burden comprises anamount of time spent in atrial fibrillation by the user during a periodof time.

In certain embodiments, the atrial fibrillation burden comprises anamount of time spent in atrial fibrillation by the user during a sleepperiod and during a wake period.

In certain embodiments, the hardware processor is configured to processthe detected cardiac signals through the first subset of layers of theneural network continuously in substantially real-time of detecting ECGsignals.

In certain embodiments, the output of the first subset of layers of theneural network comprises an indication of an R peak.

In certain embodiments, the computing system is further configured toreconstruct the cardiac signals via the second subset of layers.

Some embodiments include a system for training a neural network to infera likelihood of a characteristic of a user, the system comprising: awearable device comprising: an assembly comprising an electrodeconfigured to detect cardiac signals from a user; a hardware processorconfigured to process the detected cardiac signals through a firstsubset of layers of a neural network; and a transmitter, the transmitterconfigured to transmit the output of the first subset to a computingsystem, the computing system configured to infer a likelihood of anoccurrence of cardiac arrhythmia by processing the output through asecond subset of layers of the neural network, wherein the system isconfigured to train the neural network by: training a first neuralnetwork to identify a first feature by processing first training data ofa first time period through the first neural network; freezing weightsof the first neural network; training a second neural network toidentify a second feature by processing second training data of a secondtime period through the first and second neural network, wherein thesecond time period is longer than the first time period; unfreezingweights of the first neural network; and training the first and secondneural network simultaneously to identify the second feature byprocessing third training data of a third time period through the firstand second neural network, wherein the third time period is longer thanthe first time period.

In certain embodiments, the output of the first subset of layers of theneural network is of a smaller dimensionality than the input of thefirst subset of layers of the neural network.

In certain embodiments, the hardware processor is configured to selectthe neural network from a plurality of neural networks based on acharacteristic.

In certain embodiments, the characteristic comprises at least one of: acharacteristic of the wearable device, a network characteristic betweenthe wearable device and the computing system.

In certain embodiments, the hardware processor is further configured tocompress the output of the first subset of layers of the neural network,wherein to transmit the output of the first subset to the computingsystem comprises transmitting the compressed output, wherein thecomputing system is configured to decompress the transmitted data; andwherein processing the output through the second subset of layerscomprises processing the decompressed data.

In certain embodiments, the hardware processor is further configured toquantize the output of the first subset of layers of the neural network,wherein to transmit the output of the first subset to the computingsystem comprises transmitting the output.

In certain embodiments, the computing system infers the QT interval byreconstructing the ECG signal from the output of the second subset oflayers of the neural network.

In certain embodiments, the computing system infers the QT intervalbased on an average QT interval from a window of encoded features thatinclude a plurality of QT intervals.

Some embodiments include a wearable device for monitoring bio-signaldata of a user, the wearable device comprising: an adhesive assemblycomprising a housing and a wing, the wing comprising an electrodeconfigured to detect cardiac signals from a user; a hardware processorconfigured to process the detected cardiac signals through a firstsubset of layers of a neural network; and a transmitter, the transmitterconfigured to transmit the output of the first subset to a computingsystem, the computing system configured to generate a template beatbased on a plurality of beats over a period of time.

Some embodiments include a wearable device for monitoring bio-signaldata of a user, the wearable device comprising: an assembly comprisingan electrode configured to detect cardiac signals from a user; ahardware processor configured to process the detected cardiac signalsthrough a first subset of layers of a neural network, the output of thefirst subset of layers of the neural network comprises RR peakintervals, wherein an RR peak interval comprises a duration between twoconsecutive R-peaks; and a transmitter, the transmitter configured totransmit the output of the first subset to a computing system, thecomputing system configured to infer a likelihood of an occurrence ofcardiac arrhythmia by processing the RR peak intervals in the output ofthe first subset of layers of the neural network through a second subsetof layers of the neural network.

In certain embodiments, first subset of layers of the neural networkgenerates a sequence of RR peak intervals by extracting RR-intervalsub-sequences using an overlapping sliding window and shifting thesliding window on the detected cardiac signals.

These and other aspects and embodiments of the invention are describedin greater detail below, with reference to the drawing figures.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A and 1B are perspective and exploded profile views,respectively, of a physiological monitoring device, according to oneembodiment.

FIGS. 2A and 2B are top perspective and bottom perspective views,respectively, of a printed circuit board assembly of the physiologicalmonitoring device, according to one embodiment.

FIGS. 3A, 3B, 3C, 3D, and 3E are perspective and exploded views of aflexible body and gasket of the physiological monitoring device,according to one embodiment.

FIG. 4 is an exploded view of a rigid housing of the physiologicalmonitoring device; according to one embodiment.

FIGS. 5A and 5B provide a perspective view of a battery holder of thephysiological monitoring device, according to one embodiment.

FIGS. 6A and 6B are cross sectional views of the physiologicalmonitoring device, according to one embodiment.

FIG. 7 is an exploded view of the physiological monitoring deviceincluding a number of optional items, according to one embodiment.

FIGS. 8A and 8B are perspective views of two people wearing thephysiological monitoring device, illustrating how the device bends toconform to body movement and position, according to one embodiment.

FIGS. 9A, 9B, 9C, 9D, 9E, and 9F illustrate various steps for applyingthe physiological monitor to a patient's body, according to oneembodiment.

FIG. 10 illustrates a schematic diagram of an embodiment of a cardiacrhythm inference service.

FIG. 11 is a schematic diagram of an embodiment of a system forextracting and transmitting data features from a physiological monitor.

FIG. 12 is a schematic diagram of an embodiment of a system forextracting and transmitting data features from a physiological monitorusing a transmitting device.

FIG. 13 is a schematic diagram of an embodiment of a physiologicalmonitoring system utilizing additional data channels.

FIG. 14 is a schematic diagram of an embodiment of a physiologicalmonitoring system incorporating data filters.

FIG. 15 is a schematic diagram of an embodiment of a wearable devicesystem.

FIG. 16 is a schematic diagram of an embodiment of a symptomatictransmission system.

FIG. 17 is a schematic diagram of an embodiment of an asymptomatictransmission system.

FIG. 18A is a graph of an embodiment illustrating reading from ECGstrips corresponding to NSR rhythms.

FIG. 18B is a graph of an embodiment illustrating reading from ECGstrips corresponding to NSR rhythms.

FIG. 19 is a high level architecture of the burden prediction model.

FIG. 20 is an embodiment of the high level architecture of FIG. 19.

FIG. 21 is an embodiment of the feature extraction model of FIG. 19.

FIG. 22A is a schematic diagram of an embodiment of a system forpredicting rhythm annotations using neural network encoding.

FIG. 22B is a schematic diagram of an embodiment of a first and secondsubset of layers within a single neural network.

FIG. 22C is a schematic diagram of an embodiment of a system forprocessing a subset of layers in a neural network on the patch.

FIG. 22D is a schematic diagram of an embodiment for transmitting theoutput of a first subset of layers to a server.

FIG. 22E is a schematic diagram of an embodiment of designing (and/ortraining) the neural network.

FIG. 22F is a schematic diagram of an embodiment for designing,training, and/or selecting a neural network based on a current activityof the user.

FIG. 23 is a schematic diagram of an embodiment of a computer networksystem.

FIG. 24 is a schematic diagram of an embodiment of a programming anddistribution module.

FIG. 25 illustrates an implementation of lossless compression with theECG encoder.

FIG. 26 illustrates an embodiment of a quantizer to perform quantizationon the output data of the ECG encoder after processing the first subsetof layers in the neural network.

FIG. 27 illustrates an embodiment with both the quantizer and thelossless compression via the lossless compressor and the losslessdecompressor.

FIGS. 28A and 28B illustrate an embodiment of the process for generatinga template beat.

DETAILED DESCRIPTION OF EMBODIMENTS

The following description is directed to a number of variousembodiments. The described embodiments, however, may be implementedand/or varied in many different ways. For example, the describedembodiments may be implemented in any suitable device, apparatus, orsystem to monitor any of a number of physiological parameters. Forexample, the following discussion focuses primarily on long-term,patch-based cardiac rhythm monitoring devices. In one alternativeembodiment, a physiological monitoring device may be used, for example,for pulse oximetry and diagnosis of obstructive sleep apnea. The methodof using a physiological monitoring device may also vary. In some cases,a device may be worn for one week or less, while in other cases, adevice may be worn for at least seven days and/or for more than sevendays, for example between fourteen days and twenty-one days or evenlonger.

Since abnormal heart rhythms or arrhythmias can often be due to other,less serious causes, a key challenge is to determine when any of thesesymptoms are due to an arrhythmia. Oftentimes, arrhythmias occurinfrequently and/or episodically, making rapid and reliable diagnosisdifficult. Currently, cardiac rhythm monitoring is primarilyaccomplished through the use of devices, such as Holter monitors, thatuse short-duration (less than 1 day) electrodes affixed to the chest.Wires connect the electrodes to a recording device, usually worn on abelt. The electrodes need daily changing and the wires are cumbersome.The devices also have limited memory and recording time. Wearing thedevice interferes with patient movement and often precludes performingcertain activities while being monitored, such as bathing. Further,Holter monitors are capital equipment with limited availability, asituation that often leads to supply constraints and correspondingtesting delays. These limitations severely hinder the diagnosticusefulness of the device, the compliance of patients using the device,and the likelihood of capturing all important information. Lack ofcompliance and the shortcomings of the devices often lead to the needfor additional devices, follow-on monitoring, or other tests to make acorrect diagnosis.

Current methods to correlate symptoms with the occurrence ofarrhythmias, including the use of cardiac rhythm monitoring devices,such as Holter monitors and cardiac event recorders, are often notsufficient to allow an accurate diagnosis to be made. In fact, Holtermonitors have been shown to not lead to a diagnosis up to 90% of thetime (“Assessment of the Diagnostic Value of 24-Hour AmbulatoryElectrocardiographic Monitoring”, by DE Ward et al. Biotelemetry PatientMonitoring, vol. 7, published in 1980).

Additionally, the medical treatment process to actually obtain a cardiacrhythm monitoring device and initiate monitoring is typically verycomplicated. There are usually numerous steps involved in ordering,tracking, monitoring, retrieving, and analyzing the data from such amonitoring device. In most cases, cardiac monitoring devices used todayare ordered by a cardiologist or a cardiac electrophysiologist (EP),rather than the patient's primary care physician (PCP). This is ofsignificance since the PCP is often the first physician to see thepatient and determine that the patient's symptoms could be due to anarrhythmia. After the patient sees the PCP, the PCP will make anappointment for the patient to see a cardiologist or an EP. Thisappointment is usually several weeks from the initial visit with thePCP, which in itself leads to a delay in making a potential diagnosis aswell as increases the likelihood that an arrhythmia episode will occurand go undiagnosed. When the patient finally sees the cardiologist orEP, a cardiac rhythm monitoring device will usually be ordered. Themonitoring period can last 24 to 48 hours (Holter monitor) or up to amonth (cardiac event monitor or mobile telemetry device). Once themonitoring has been completed, the patient typically must return thedevice to the clinic, which itself can be an inconvenience. After thedata has been processed by the monitoring company or by a technicianon-site at a hospital or office, a report will finally be sent to thecardiologist or EP for analysis. This complex process results in fewerpatients receiving cardiac rhythm monitoring than would ideally receiveit.

To address some of these issues with cardiac monitoring, the assignee ofthe present application developed various embodiments of a small,long-term, wearable, physiological monitoring device. One embodiment ofthe device is the Zio® Patch. Various embodiments are also described,for example, in U.S. Pat. Nos. 8,150,502, 8,160,682 8,244,335,8,560,046, and 8,538,503, the full disclosures of which are herebyincorporated herein by reference. Generally, the physiologicalpatch-based monitors described in the above references fit comfortablyon a patient's chest and are designed to be worn for at least one weekand typically two to three weeks. The monitors detect and record cardiacrhythm signal data continuously while the device is worn, and thiscardiac rhythm data is then available for processing and analysis.

Physiological Monitoring Devices

Referring to FIGS. 1A and 1B, perspective and exploded profile views ofone embodiment of a physiological monitoring device 100 are provided. Asseen in FIG. 1A, physiological monitoring device 100 may include aflexible body 110 coupled with a watertight, rigid housing 115, and ahinge portion 132. Flexible body 110 (which may be referred to as“flexible substrate” or “flexible construct”) typically includes twowings 130, 131, which extend laterally from rigid housing 115, and twoflexible electrode traces 311, 312, each of which is embedded in one ofwings 130, 131. Each electrode trace 311, 312 is coupled, on the bottomsurface of flexible body 110, with a flexible electrode (not visible inFIG. 1A). The electrodes are configured to sense heart rhythm signalsfrom a patient to which monitoring device 100 is attached. Electrodetraces 311, 312 then transmit those signals to electronics (not visiblein FIG. 1A) housed in rigid housing 115. Rigid housing 115 alsotypically contains a power source, such as one or more batteries.

Referring now to FIG. 1B, a partially exploded view of physiologicalmonitoring device 100 illustrates component parts that make up, and thatare contained within, rigid housing 115 in greater detail. In thisembodiment, rigid housing 115 includes an upper housing member 140,which detachably couples with a lower housing member 145. Sandwichedbetween upper housing member 140 and lower housing member 145 are anupper gasket 370, and a lower gasket 360 (not visible on FIG. 1B butjust below upper gasket 370). Gaskets 370, 360 help make rigid housingmember 115 watertight when assembled. A number of components ofmonitoring device 100 may be housed between upper housing member 140 andlower housing member 145. For example, in one embodiment, housing 115may contain a portion of flexible body 110, a printed circuit boardassembly (PCBA) 120, a battery holder 150, and two batteries 160.Printed circuit board assembly 120 is positioned within housing 115 tocontact electrode traces 311, 312 and batteries 160. In variousembodiments, one or more additional components may be contained withinor attached to rigid housing 115. Some of these optional components aredescribed further below, in reference to additional drawing figures.

Battery holder 150, according to various alternative embodiments, mayhold two batteries (as in the illustrated embodiment), one battery, ormore than two batteries. In other alternative embodiments, other powersources may be used. In the embodiment shown, battery holder 150includes multiple retain tabs 153 for holding batteries 160 in holder150. Additionally, battery holder 150 includes multiple feet 152 toestablish correct spacing of batteries 160 from the surface of PCBA 120and ensure proper contact with spring fingers 235 and 236. Springfingers 235 and 236 are used in this embodiment rather than solderingbatteries 160 to PCBA 120. Although soldering may be used in alternativeembodiments, one advantage of spring fingers 235 and 236 is that theyallow batteries 160 to be removed from PCBA 120 and holder 150 withoutdamaging either of those components, thus allowing for multiple reusesof both Eliminating solder connections also simplifies and speeds upassembly and disassembly of monitoring device 100. In some embodiments,upper housing member 140 may act as a patient event trigger.

Referring now to the embodiments in FIGS. 2A and 2B, printed circuitboard assembly 120 (or PCBA) may include a top surface 220, a bottomsurface 230, a patient trigger input 210 and spring contacts 235, 236,and 237. Patient trigger input 210 may be configured to relay a signalfrom a patient trigger, such as upper housing member 140 describedabove, to PCBA 120. For example, patient trigger input 210 may be a PCBswitch or button that is responsive to pressure from the patient trigger(for example, the upper surface of upper housing portion 140).

With reference now to the embodiments of FIGS. 3A and 3B, flexible body110 is shown in greater detail. As illustrated in FIG. 3A, flexible body110 may include wings 130, 131, a thin border 133 (or “rim” or “edge”)around at least part of each wing 130, 131, electrode traces 311, 312,and a hinge portion 132 (or “shoulder”) at or near a junction of eachwing 130, 131 with rigid housing 115.

As illustrated in the embodiments of FIGS. 3C and 3D, ECG circuitinterface portions 313 are in physical contact with spring fingers 237and provide electrical communication with PCBA 120 when device 100 orzoomed-in device portion 101 is assembled. Electrode interface portions310 contact hydrogel electrodes 350. Thus, electrode traces 311, 312transmit cardiac rhythm signals (and/or other physiological data invarious embodiments) from electrodes 350 to PCBA 120.

FIG. 3E depicts yet another embodiment where top gasket 370 includestabs 371 that protrude away from the profile of top housing 140 whilestill being adhered to upper substrate 300. The tabs 371 cover a portionof electrode traces 311, 312 and provide a strain relief for the tracesat the point of highest stress where the flexible body meets the rigidhousing.

With reference now to the embodiment of FIG. 4, upper housing member 140and lower housing member 145 of rigid housing 115 are shown in greaterdetail. Upper and lower housing members 140, 145 may be configured, whencoupled together with gaskets 360, 370 in between, to form a watertightenclosure for containing PCBA 120, battery holder 150, batteries 160 andany other components contained within rigid housing 115. Housing members140, 145 may be made of any suitable material to protect internalcomponents, such as water resistant plastic.

Referring now to the embodiment of FIG. 5A, battery holder 150 is shownin greater detail. Battery holder 150 may be made of plastic or othersuitable material, is configured to be mounted to PCBA 120 andsubsequently attached to rigid housing 115, and is capable of holdingtwo batteries 160 (FIG. 1B). A plurality of protrusions 152 provide astable platform for batteries 160 to be positioned a fixed distanceabove the surface of PCBA 120, avoiding unwanted contact with sensitiveelectronic components yet providing for adequate compression of springcontacts 235 (FIG. 5B).

With reference now to the embodiments of FIGS. 6A and 6B, physiologicalmonitoring device 100 is shown in side view cross-section. As shown in6A, physiological monitoring device 100 may include flexible body 110coupled with rigid housing 115. Flexible body 110 may include topsubstrate layer 300, bottom substrate layer 330, adhesive layer 340 andelectrodes 350. Electrode traces 311, 312 are also typically part offlexible body 110 and are embedded between top substrate layer 300 andbottom substrate layer 330, but they are not shown in FIG. 6. Flexiblebody 110 forms two wings 130, 131, extending to either side of housing115, and a border 133 surrounding at least part of each wing 130, 131.Rigid housing 115 may include an upper housing member 140 coupled with alower housing member 145 such that it sandwiches a portion of flexiblebody 110 in between and provides a watertight, sealed compartment forPCBA 120. Upper housing member 140 may include inner trigger member 430,and PCBA may include patient trigger member 210. As discussedpreviously, lower housing member 145 may include multiple dimples 450 ordivots to enhance the comfort of the monitoring device 100. In certainembodiments, an additional mechanism to reduce and prevent unwantedbending of PCBA 120 may be used. This mechanism is shown in FIG. 6B.

Referring to FIG. 7, in some embodiments, physiological monitoringdevice 100 may include one or more additional, optional features. Forexample, in one embodiment, monitoring device 100 may include aremovable liner 810, a top label 820, a device identifier 830 and abottom label 840.

Referring now to the embodiments of FIGS. 8A and 8B, physiologicalmonitoring device 100 generally includes hinge portions 132 at or nearthe juncture of each wing 130, 131 with rigid housing 115. Additionally,each wing 130, 131 is typically adhered to the patient via adhesivelayers 340, while rigid body 115 is not adhered to the patient and isthus free to “float” (for example, move up and down) over the patient'sskin during movement and change of patient position. In other words,when the patient's chest contracts, rigid housing pops up or floats overthe skin, thus minimizing stress on device 100, enhancing comfort, andreducing the tendency of wings 130, 131 to peel off of the skin.

Referring now to FIGS. 9A-9F, one embodiment of a method for applyingphysiological monitoring device 100 to the skin of a human subject isdescribed. In this embodiment, before the first step shown in FIG. 9A,the patient's skin may be prepared, typically by shaving a small portionof the skin on the left chest where device 100 will be placed and thenabrading and/or cleaning the shaved portion. As shown in FIG. 9A, oncethe patient's skin is prepared, a first step of applying device 100 mayinclude removing one or both of two adhesive covers 600 from adhesivelayers 340 on the bottom surface of device 100, thus exposing adhesivelayers 340. As illustrated in FIG. 9B, the next step may be to applydevice 100 to the skin, such that adhesive layer 340 adheres to the skinin a desired location. Referring to FIG. 9C, after device 100 has beenapplied to the skin, pressure may be applied to flexible body 110 topress it down onto the chest to help ensure adherence of device 100 tothe skin.

In a next step, referring to FIG. 9D, liner 810 is removed from (forexample, peeled off of) the top surface of flexible body 110. As shownin FIG. 9E, once liner 810 is removed, pressure may again be applied toflexible body 110 to help ensure it is adhered to the skin. Finally, asshown in FIG. 9F, upper housing member 140 may be pressed to turn onphysiological monitoring device 140. This described method is only oneembodiment. In alternative embodiments, one or more steps may be skippedand/or one or more additional steps may be added.

The physiological monitors described above and elsewhere in thespecification may further be combined with methods and systems of dataprocessing and transmission that improve the collection of data from themonitor. Further, the methods and systems described below may improvethe performance of the monitors by enabling timely transmission ofclinical information while maintaining the high patient compliance andease-of-use of the monitor described above. For example, the methods andsystems of data processing and transmission described herein thissection of elsewhere in the specification may serve to extend thebattery life of the monitor, improve the accuracy of the monitor, and/orprovide other improvements and advantages as described herein thissection or elsewhere in the specification.

Device Monitoring and Clinical Analysis Platform

The systems and methods described in detail below, in reference to theembodiments of FIGS. 10 to 17, may selectively extract, transmit, andanalyze electrocardiographic signal data and other physiological datafrom a wearable physiological monitor, such as is described above inrelation to FIGS. 1 through 9. The systems and methods described belowcan improve the performance of a wearable physiological monitor thatsimultaneously records and transmits data through multiple means. Forexample, selective transmission of extracted data allows for decreasedpower consumption because the wearable patch is not required to transmitall recorded data. By sending extracted data, much of the analysis maybe performed away from the wearable device without requiring fullon-board rhythm analysis, which can also be highly power consumptive,reducing battery life. Further, remote analysis without the powerconstraints inherent to a wearable device may allow for greatersensitivity and accuracy in analysis of the data. Decreased powerconsumption serves to improve patient compliance because it prolongs thetime period between or even eliminates the need for device replacement,battery changes or battery recharging during the monitoring cycle. Bydecreasing battery consumption, longer monitoring times may be enabledwithout device replacement, for example, at least one week, at least twoweeks, at least three weeks, or more than three weeks.

FIG. 10 depicts a general overview of an embodiment of a system 900 forinferring cardiac rhythm information from an R-R interval time series902, as may be generated by a continuous heart rate monitoring device904. The R-R interval time series 902 inputted to the system may includea series of measurements of the timing interval between successiveheartbeats. The R-R interval time series 902 data may be extracted fromor received from a dedicated heart rate monitor such as a heart ratechest strap or heart rate watch, or a wearable health or fitness device906, 908 that incorporates heart rate sensing functionality.Alternatively, the R-R interval time series 902 may be derived from awearable patch designed to measure an ECG signal 904 (for instance, bylocating the R peaks in the ECG using a QRS detection algorithm).Furthermore, the R-R interval time series 902 may be estimated from analternative physiological signal such as that obtained fromphotoplethysmography (PPG). In this scenario, the peak-to-peak intervaltime series determined from the PPG signal may be used as an accurateestimate of the R-R interval time series.

In certain embodiments, a cardiac rhythm inference system may accept aplurality of R-R interval time series measured from devices of a givenuser 918, in addition to an individual R-R interval time series 902. Inparticular embodiments, a cardiac rhythm inference system 910 may acceptadditional sources of data, generally described as alternate sensorchannels, in addition to R-R interval time series data, to enhance theaccuracy and/or value of the inferred results.

Current wearable sensors, such as the iRhythm ZioPatch™ 904, and furtherdescribed above in relation to FIGS. 1-9, are capable of recording asingle-lead electrocardiogram (ECG) signal for up to two weeks on asingle battery charge.

Extraction, Transmission, and Processing Systems

FIG. 11 is a schematic illustration of an embodiment of a system andmethod 1000 for a wearable medical sensor 1002 with transmissioncapabilities, similar to the system and/or method described above inrelation to FIG. 10. In some embodiments, sensor 1002, which may be anytype of sensor or monitor described herein this section or elsewhere inthe specification, continuously senses an ECG or comparable biologicalsignal 1004 and continuously records an ECG or comparable biologicalsignal 1004. The collected signal 1004 may then be continuouslyextracted into one or more features 1006, representing example featuresA, B, and C. The features of the ECG or comparable biological signal areextracted to facilitate analysis of the signal 1004 remotely.

Once the feature extraction as described above is completed, variousfeatures 1008 may then be transmitted 1010 to a processing device/server1012. The features 1008 (and alternate sensor channel data and/orfeatures as described below) are transmitted 1010 at regular intervalsto a processor 1012 that is not a physical part of the sensor 1002.

In some embodiments, the transmitted features 1014 are processed by theremote processor utilizing the data features 1014 to perform analysisvia a rhythm inference system 1016 that analyzes and identifiessegments/locations 1018 likely to include arrhythmia. For example,arrhythmia and ectopy types that may be identified could include: Pause,2^(nd) or 3^(rd) degree AVB, Complete Heart Block, SVT, AF, VT, VF,Bigeminy, Trigeminy, and/or Ectopy. Confidence of determination may beincluded in the identification of rhythms.

The identified arrhythmia locations 1018 are then transmitted 1020 backto the sensor 1002. The transmission 1020 back to the sensor may beaccomplished by any communication protocols/technology described hereinthis section or elsewhere in the specification, for example viaBluetooth. The sensor then reads the transmitted identified locations1022 and accesses 1024 the areas of memory corresponding to thetransmitted identified locations 1022 of the ECG. In some embodiments,the sensor applies additional analysis of the identified segments tofurther build confidence in the arrhythmia identification. This furtherrhythm confidence determination step 1026 allows for increasing positivepredictivity prior to the power-hungry transmission step. Inembodiments, if the confidence exceeds a defined threshold the datasegment is transmitted. If the confidence exceeds a threshold asdescribed above, the sensor 1002 may transmit the requested ECG segments1028 to the processing device via any transmission means describedherein this section or elsewhere in the specification.

FIG. 12 is a schematic illustration of an embodiment of a system andmethod 2000 for a wearable ECG and/or medical sensor 2002 withtransmission capabilities very similar to the system and/or method 1000described above in relation to FIG. 11. The system of FIG. 12 differsfrom the system of FIG. 11 in that it includes secondary transmittingdevices 2004.

FIG. 13 is a schematic illustration of an embodiment of a system andmethod 3000 for a wearable ECG and/or medical sensor 3002 withtransmission capabilities, very similar to the system and/or methodsdescribed above in relation to FIGS. 11 and 12. FIG. 13 differs fromFIGS. 11 and 12 in that FIG. 13 illustrates alternate sensor channels3004, 3006 producing alternate outputs and/or extraction 3008 offeatures 3010. Collection of other channels of data may serve to furtheraugment ECG-extracted features. Data from the alternate sensor channelsmay be sent whole or specific features 3010 of the data channel may beextracted 3008.

FIG. 14 is a schematic illustration of an embodiment of a system andmethod 4000 for a wearable ECG and/or medical sensor 4002 withtransmission capabilities, very similar to the system and/or methodsdescribed above in relation to FIGS. 11 to 13. FIG. 14 differs fromFIGS. 11 to 13 because the embodiment of FIG. 14 incorporates additionaldata filters. In some embodiments, the Processing Device 4004 may alsofilter rhythms 4006 identified by the rhythm inference system 4008 byapplying filter criteria that may derive from multiple sources.

FIG. 15 is a schematic illustration of an embodiment of a system 5000for a consumer wearable device without full ECG detection, with somesimilarities to the medical sensors of FIGS. 10 to 14. The sensors 5002need not be medical-grade ECG sensors, but merely allow detection ofbeats. In embodiments, the sensor 5002 may continuously sense a datachannel from which heart beat locations can be derived. Possible datasources include: PPG (optionally with multiple channels to increaseaccuracy), bio-impedance, and ECG without full implementation due toinsufficient signal quality as compared to the sensors of FIGS. 10 to14. Similar to the devices of FIGS. 10 to 14, features may be extractedfrom this signal, for example: R-peak locations, R-peak overflow flag,saturation flag, breathing rate, P/T wave locations, R-peak amplitude(or proxy), or ECG signal amplitude proxy.

The consumer device system 5000 without full ECG sensing advantageouslyenables arrhythmia analysis using consumer-available heart-rate sensors,thereby reducing the cost and increasing the availability of the device.Consequently, this may enable arrhythmia screening on a largerpopulation, including via over-the-counter screening.

FIG. 16 is a schematic diagram of an embodiment of an ECG monitor system6000 with symptomatic transmission. Such a system would involve awearable ECG sensor, similar to the sensors described in relation toFIGS. 1 to 14. As described above, such a sensor senses and records ECGcontinuously. Each symptom trigger by a patient may initiate transfer ofan ECG data strip.

FIG. 17 is a schematic diagram of an embodiment of an ECG monitor system7000 with both symptomatic and asymptomatic transmission. In someembodiments, each asymptomatic trigger initiates transfer of an ECG datastrip such as described above.

Systems for Estimating Burden from R-peak Sequences using NeuralNetworks

Some embodiments disclose a wearable device. The wearable device cancomprise sensors for detecting bio-signals of a user, such as ECGsignals. The wearable device can process the detected bio-signalsthrough a first subset of layers of a neural network. The wearabledevice can take the output of a first subset of layers of a neuralnetwork, and transmit the output to a computing device (e.g., anexternal system such as a smart phone or a server) to further processthe data through a second subset of layers of the same neural network inorder to derive a characteristic of the user, such as an indication ofpast cardiac arrhythmia and/or predict a future onset of arrhythmia. Insome embodiments, the computing device is a processor external to thewearable device or a processor within the wearable device. In certainembodiments, as described herein, at least some of the computation mayoccur on one more processors external to the wearable device and/or oneor more processors within the wearable device.

In some embodiments, the output of the first subset of layers are anindication of R peak sequences. In other embodiments, the output of thefirst subset of layers are an indication of RR intervals. The wearabledevice can process the ECG signal through the first subset of layers ofthe neural network to receive R peak sequences from the output of thefirst subset of layers. The wearable device can transmit the R peaksequences to an external computing device. The external computing devicecan process the R peak sequences through the second subset of layers ofthe neural network, and receive as an output of the second subset oflayers a derived characteristic of the user (such as the indication ofpast cardiac arrhythmia and/or predict a future onset of arrhythmia,burden of atrial fibrillation, etc).

There has been a lot of recent activity exploring the usage of wearablesensors to detect cardiac arrhythmias like atrial fibrillation (AFib).The wearable sensors used for such studies fall broadly into twocategories. The first category of devices are electrocardiographic (ECG)monitoring devices. The second category of devices arephotoplethysmography (PPG) based devices. When using PPG-based devicesfor AFib detection, the results have been mixed. For example, in onestudy, utilizing the heart rate and step count data from a PPG baseddevice, a deep neural network based algorithm on a dataset where PPGdata was obtained from a patient cohort immediately before and aftercardioversion for AFib in a clinical setting resulted in decent results,but the same approach and algorithm had a more modest performance on amuch broader ambulatory cohort of patients with persistent AFib. Thereasons for the poor performance on the ambulatory cohort of patientscould be numerous but the most influential factor is very likely thepoor PPG signal quality for heart-rate estimation in uncontrolledsettings. This raises the need to detect AFib more accurately usingheart rate data derived from ECG signals recorded using a wearablecardiac monitor like the ECG monitoring devices.

AFib burden is the fraction of time that a patient spends in the AFibstate. Recently, the notion of AFib burden has gained a lot of attentiondue to studies demonstrating the association of AFib burden and the riskof ischemic stroke in adults. Therefore cardiac monitoring solutionswhere one can quickly and accurately estimate the AFib burden of apatient can lead to meaningful medical interventions for patients whohave been identified with having a higher risk for stroke.

For clinical analysis, a patient's ECG signal recorded using a cardiacmonitoring device is typically segmented into episodes of varyingdurations and of different rhythm types. A normal, healthy patient willtypically be in a state of a normal rhythm referred to as Normal SinusRhythm (NSR). Various heart conditions can manifest as patterns ofirregular/abnormal heart beats known as cardiac arrhythmias which inturn translate into irregular/abnormal patterns in the ECG signal. Thereare various types of cardiac arrhythmias such as atrial fibrillation. Apatient could switch back and forth between a normal rhythm and one/manyof the different types of cardiac arrhythmias or could even beconstantly in one of these abnormal rhythms like AFib. AFib burden isthe fraction of time that a patient spends in a state of atrialfibrillation. Atrial flutter is another arrhythmia that is closelyrelated to AFib and increases the risk for stroke. AFib and atrialflutter are at times difficult to discern from each other and aretypically grouped together for burden calculations due to similarclinical significance. Therefore, for the purposes of this disclosure,embodiments will be described in reference to AFib, but it is understoodthat some embodiments can apply also (or only) to atrial flutter.

The QRS complex is a dominant feature of an ECG signal. The peak withinthe QRS complex is commonly referred to as the R-peak. Some embodimentsdetect R-peak signals from the ECG signal. Once R-peaks are detected,the instantaneous heart rate can be estimated as the inverse of theduration between two consecutive R-peaks (referred to as theRR-interval). A key signature of AFib episodes is high RR-intervalvariability.

FIG. 18A is a graph of an embodiment illustrating reading from ECGstrips corresponding to NSR rhythms. FIG. 18B is a graph of anembodiment illustrating reading from ECG strips corresponding to NSRrhythms. The dots indicate the location of the R-peaks.

The ECG signal recorded from a patient can be segmented into episodes ofdifferent rhythm types and then the AFib burden can be compute bycalculating the fraction of time spent in the AFib state. However, ifthe computation is based only on the extracted sequence of R-peaklocations (and not the whole ECG signal), estimating the AFib burdenbecomes a different algorithmic problem. A scenario where such analgorithm could be useful is when we might want to avoid excessivecomputations on the monitoring device itself (using the entire ECGsignal) or when we might want to limit the amount of data that istransmitted from the monitoring device using wireless communications tocomputer servers where the AFib burden is calculated.

Described herein are embodiments that detect AFib reliably using heartrate data obtained from a wearable cardiac monitor. Some embodiments canalso estimate the daily AFib burden of patients reliably.

In some embodiments, the algorithm (e.g., a neural network) can estimateAFib burden over shorter analysis windows (e.g., duration in the orderof a half hour or one hour).

In some embodiments, the burden over a longer period of time (e.g.,longer than the shorter analysis windows) can be determined byaccumulating the burden predictions over the analysis windows. Forexample, the burden can be accumulated over non-overlapping analysiswindows of half-hour duration. Given the R-peak sequence over ananalysis window of half-hour, the RR-interval sequence within theanalysis window is derived. This RR-interval sequence is thentransformed into a sequence of RR-interval sub-sequences by extractingthe RR-interval sub-sequences from a set of possibly overlapping slidingwindows of duration w (contained with the analysis window) and shiftedby an amount s. The RR-interval sub-sequence obtained from each slidingwindow is padded/truncated so that all sub-sequences have the samelength M. For example, if there are N_(w) such sliding windows withinthe analysis window, the dimension for the temporal sequence is (N_(w),M). The temporal sequence feeds into a neural network model for makingburden predictions. For the purposes of explaining the concept, w waschosen to be 30 seconds, s chosen to be 25 seconds and M chosen to be42. The portion of the neural network (e.g., a first subset of layers ofthe neural network) on the monitor can process the detected cardiacsignals to generate the RR-interval data (and/or the RR-intervalsub-sequences), and transmit the RR-interval data to a remote computingdevice. The remote computing device can process the RR-interval data(such as by processing through a second portion of the neural network(e.g., a second subset of layers of the neural network) to makeinferences, such as a likelihood of arrythmia. In some embodiments, theneural network can be trained to make a determination on use of thewearable device, such as an indication that the user should wear thedevice longer due to a higher risk of a potential negative health event,or that the user is at low risk and can end their wear period earlier.

Advantageously, such segregated processing of the data can have manytechnical benefits. For example, such selective transmission of datathat is partially processed can decrease power consumption for thewearable path. The wearable patch is not required to store the entiredata processing algorithm (such as the entire neural network), but mayonly need to retrieve a subset of the neural network layers to process.Thus, the data processing on the wearable device can be much faster,require less computations, and use less battery via the processing. Theanalysis on the wearable patch side may not require full on-board rhythmanalysis which can often times be very highly power consumptive,reducing battery life. Decreased battery consumption can prolong the useof the wearable device, such as the time period between devicereplacement, battery recharging, and the like. Moreover, the wearabledevice can have longer monitoring times to get a fuller picture of apatient's cardiac rhythms.

Another advantage for remote analysis based on RR-interval data mayallow for greater sensitivity and accuracy than analysis on the path.Because of the memory, processing, and battery restrictions on thewearable patch, analysis on the wearable patch can be limited by theserestrictions. Remote analysis can process the data through a widevariety of algorithms, even algorithms based on a decision tree ofdeterminations and occurrences.

Moreover, the wearable patch can send a smaller dimensionality of datathan the entire detected cardiac signal. Advantageously, less networkthroughput is required. The wearable patch can send sufficient data forremote data processing even when wireless network conditions may not beoptimal. Moreover, because only a subset of the neural network need beuploaded to the wearable patch, the wearable patch can be updated withupdated neural network computer-executable instructions or downloadableexecutable faster and under weaker network conditions, and can occurmore frequently.

Furthermore, because of the lower battery and power requirements, asmaller CPU or a smaller battery can be used to result in a much smallerform factor for the wearable patch. In addition, the reduction ofbattery and power requirements for the data processing frees the batteryand power budget for the wearable device, such that the wearable devicecan perform other critical tasks, such as running other criticalsoftware processes, or powering other hardware devices, such as moreelectrodes, other sensors such as accelerometers, or running the samehardware and software for a longer period of time.

The neural network encoding approach advantageously allows for verysophisticated algorithms to be run from data on a battery constraineddevice. One application of this would be to aggregate the encoded datafrom the device in real time, and run algorithms that will assess theoverall risk of a subject developing a certain clinical outcome. Assuch, the system can inform the user of a wear period dynamically. Forexample, if after the wearable device aggregates a certain amount ofdata, the remote computing device may determine that the subject hasvery low risk for developing a certain clinical outcome. The system caninform the user to the wear period early. Conversely, if the computingdevice has determined that the user is at a high risk for a certainclinical outcome, the system can inform the user to wear the devicelonger.

Another technical advantage of having a first subset of layers of aneural network output the RR interval data to be sent for remoteprocessing is that the algorithms for making inferences remotely can beupdated without having to update the wearable patch. For example, anupdated algorithm or neural network can be updated on the remotecomputing device via an update to the second subset of layers, whereasthe same first subset of layers can be used on the wearable device. Thetechnical advantage is that the second subset of layers can be selectedbased on a decision tree, updated or swapped with other algorithms, andthe like without having to update the wearable patch, making the systemmore compatible with other algorithms.

Example High Level Architecture of the Burden Prediction Model

FIG. 19 is a high level architecture of the burden prediction model. Theburden prediction model takes as input RR-interval sub-sequences andproduces an AFib burden prediction. Here Nw is the number of slidingwindows and M is the size of the RR-interval sub-sequence extracted fromeach sliding window.

The first layer 1902 is a temporally distributed feature extractionlayer which extracts a 32-dimensional feature vector for eachRR-interval sub-sequence obtained from the sliding windows. The outputof this layer is a transformed sequence of dimension (N_(w), 32). Thistransformed sequence then feeds into two Recurrent Neural Network (RNN)layers 1904, 1906 with Long Short Term Memory (LSTM) cells. The firstRNN layer 1904 returns another transformed sequence of dimension (N_(w),32) while the second RNN layer 1906 returns a single output of dimension32. This is followed by two Dense (fully connected) layers 1908, 1910producing a scalar value representing the AFib burden for the analysiswindow.

FIG. 20 is an embodiment of the high level architecture of FIG. 19,illustrating the LSTM architecture in more detail.

FIG. 21 is an embodiment of the feature extraction model of FIG. 19. Asshown, the model for the feature extraction can be temporallydistributed over sliding windows in the first layer of the model. Themodel for feature extraction which is temporally distributed over thesliding windows can be itself another neural network with multiplelayers.

At the start of FIG. 19 and step 2102 of FIG. 21, the feature extractionmodel takes in as input an RR-interval subsequence of length M which isprocessed by a set of one-dimensional Convolutional layers andMax-pooling layers at steps 1902, 1904, 1906 of FIG. 19 and at steps2104, 2106, 2108, 2110, 2112, 2114 of FIG. 21. This is followed by aDense (fully connected layer) which outputs a feature vector ofdimension 32 at steps 1908, 1910 of FIGS. 19 and 2120, 2122 of FIG. 21.The intent of this feature extraction model is to learn features thatare indicative of the presence/absence of AFib within each slidingwindow of duration w.

For the neural network, a loss function that can be used is the mean(over all samples) of the absolute difference between the burdenprediction coming from the model and the true burden value:

$\begin{matrix}{L = {\frac{1}{N}{\sum\limits_{i = 1}^{N}{❘{{\overset{\hat{}}{b}}_{i} - b_{i}}❘}}}} & (1)\end{matrix}$

Here, b_(i) is the burden prediction output by the model for the windowin the dataset, b_(i) is the true burden for that window and N is thetotal number of samples in the dataset.

In some embodiments, the parameters of the feature extraction network(e.g., FIG. 21) can be learned separately first. This is done by addingone more Dense (fully connected) layer with a scalar output and sigmoidactivation to the feature extracting network and then training theresulting model to do binary classification for detecting the presenceof AFib in the 30-sec ECG strips.

The parameters of the feature extracting network thus obtained are thenfrozen and the resulting feature extracting network is plugged into theburden prediction model (FIG. 20). The subsequent layers (recurrentnetwork layers and fully connected layers) of the burden predictionmodel are then trained on the half-hour windows described in Section4.2.

The feature extractor network is trained over a much larger and diverseset of ECG strips and directly learns features that are indicative ofthe presence/absence of AFib. When training the remaining layers of theburden prediction model, the number of parameters that have to belearned are reduced and therefore the search for the optimal parameterspossibly happens faster. In contrast, if the entire network were to betrained at the same time, the number of parameters that have to belearned simultaneously is larger and the model is exposed to a lessdiverse set of ECG strips. This increases the risk for over-fitting andpossibly explains the wider gap between the validation loss and thetraining loss.

Systems for Estimating Burden and/or Predicting Rhythm Annotations UsingNeural Network Encoding

Some embodiments disclose a wearable device that can process thedetected bio-signals through an encoder that includes a first subset oflayers of a neural network. FIG. 22A is a schematic diagram of anembodiment of a system for predicting rhythm annotations using neuralnetwork encoding. In some embodiments, the system can make one or morepredictions, such as predicting rhythm annotations or estimating burdenfor cardiac rhythms including Atrial Fibrillation and/or Atrial Flutter.The wearable device, such as a cardiac monitor patch 2202, can processan ECG input 2204 through a first subset of layers 2206 of a neuralnetwork, such as an encoder. The wearable device can receive the outputof a first subset 2206 of layers of neural network, and transmit theoutput to a computing device 2208 (e.g., an external system such as asmart phone or a server) to further process the data through a decoderthat includes a second subset of layers 2210 of the same neural networkin order to derive a characteristic of the user 2212, such as anindication or prediction of past cardiac arrhythmia and/or predict afuture onset of arrhythmia.

In some embodiments, the first subset and the second subset of layersare within one neural network. The neural network can be designed suchthat the output of a first subset of layers can output data at a smallerdimensionality than the input to the neural network, and the output ofthe second subset of layers can be designed to provide an indication ofa user characteristic, such as a past or future prediction of AFib. FIG.22B is a schematic diagram of an embodiment of a first and second subsetof layers within a single neural network. The neural network can betrained simultaneously on both the first subset of layers 2222 andsecond subset of layers 2224. For example, if a neural network has 10hidden layers, the first 4 layers is processed on the wearable device,such as a cardiac monitor patch 2226, the output of the 4^(th) layer,which is of lower dimensionality than the input (e.g., has good datacompression features) to the 1^(st) layer, is transmitted to an externalcomputing system, such as a server 2228. The external computing systemprocesses the output of the 4^(th) layer through the 5-10^(th) layers.The dimensionalities of each layer of the neural network can be designedto output a certain data size (e.g., output dimensions for eachconvolution or pooling layers). For example, the patch 2226 can includean ECG encoder 2230 that receives ECG data 2232 at 2400 bits per second(bps). The patch 2226 can process the ECG data 2232 through a firstsubset of layers 2222 of the neural network (such as within the ECGencoder 2230) and output a smaller dimensionality of data 2234, such asdata at 128 bps. The output data 2234 can be transmitted to an externalserver 2228. The external server 2228 can include a classifier 2238 thatprocesses the output data 2234 through a second subset of layers 2224 ofthe neural network and outputs an indication or prediction 2236 of thepatient that it is trained for. The entire neural network, including thefirst subset 2222 and the second subset 2224 of layers can be designedand trained as a single neural network.

In some embodiments, the output of the first subset of layers can be asmaller dimensionality than the input to the neural network. As such,instead of transmitting the entire ECG signal (e.g., the input to theneural network) from the wearable device to the external computingdevice, the wearable device can transmit a smaller amount of data to theexternal computing device, such as the 128 bps output data 2234 of thefirst subset of layers instead of the full 2400 bps ECG signal 2232.Advantageously, less network throughput is required to derive theindication of past cardiac arrhythmia and/or predict a future onset ofarrhythmia.

Moreover, instead of processing the ECG signal on the wearable devicethrough all layers of the neural network in order to derive theindication of past cardiac arrhythmia and/or predict a future onset ofarrhythmia, the wearable device can process the ECG signal through onlya first subset of the layers (such as through an encoder) of the neuralnetwork via the ECG encoder 2230, and transmit the output of the firstsubset to an external device 2228 that processes the second subset oflayers (such as through a decoder or classifier 2238). FIG. 22C is aschematic diagram of an embodiment of a system for processing a subsetof layers in a neural network on the patch. The patch 2242 can processall layers of the neural network 2244 to receive an indication ofprediction of AFib. However, such processing can require high memory andpower requirements (which in turn can lead to a larger form factor, alarger sized patch) and increased battery usage leading to shorter usetime. However, in other embodiments, the patch 2246 can process a firstsubset of layers 2248 of a neural network, and an external server 2250can process a second subset of layers. Advantageously, the wearabledevice performs a smaller amount of computations and requires lessmemory to store data between layers.

Moreover, the wearable device 2246 can be smaller in size due to asmaller battery, processor, and other electronic devices required toprocess only a subset of layers of the neural network. The wearabledevice can have more resources to perform other tasks. For example, inone scenario, the wearable device 2242 may have enough computationalpower and battery to process the ECG signal internally and identify AFibfor 5 days. However if the wearable device 2246 could compute the firstsubset of layers of the neural network on the wearable device 2246 andthe external computing device 2250 process the second subset, the systemas a whole may be able to identify both AFib and atrial flutter over thespan of 20 days. Some embodiments disclosed herein can reduce networkthroughput, reduce computational requirements, and/or memory storage ofthe wearable device. Advantageously, the wearable device can preservebattery life by using less of the processor.

Certain disclosed embodiments herein improve on memory requirements ofthe wearable device. If the wearable device is to process a first subsetof layers of the neural network and the external computing system thesecond subset, the wearable device has to store less data (e.g.,software executable instructions for the first subset of layers and notthe entire neural network). Thus, the electronic files are much smallerin size. Advantageously, the wearable device not only has less memoryrequirements, but the neural network can also be loaded and processedfaster than the full neural network. Moreover, updating the softwareembedded within the wearable devices can occur much faster, under weakernetwork connection conditions, and can be programmed to occur morefrequently. Furthermore, more neural networks can be stored in thewearable device. For example, instead of a single full neural networkbeing stored in memory, the wearable patch can store the first subset oflayers for 5 neural networks in local memory.

In some embodiments, because the purpose of the external computingdevice is not to reconstruct the ECG signal but rather to derive someuser characteristic (e.g., predict rhythm classifications such as AFib),the data that is transmitted from the wearable device does not have tobe sufficient to recover the full fidelity of the ECG signal. Thus, theneural network can be trained to generate an output in one of its layersthat is a smaller dimensionality than the ECG signal input, and transmitmuch less to the external computing system to perform the other layersof the neural network. FIG. 22D is a schematic diagram of an embodimentfor transmitting the output of a first subset of layers to a server. Thepatch 2262 can transmit the full ECG signal (e.g., at 2400 bps), orother characteristics of data to the server 2264. However, such data canbe hacked and sensitive data retrieved by an unexpected third party.Some embodiments disclose processing a subset of layers of the neuralnetwork on the wearable device, which provide a technical advantage overprocessing the entire ECG signal remotely. Transmitting the entire ECGsignal at 2400 bps, or a combination of R peaks, R intervals, and othercharacteristics, from the wearable device to the external computingsystem can require more network bandwidth than if the wearable devicetransmits the output of the subset of layers that are of smallerdimensionality than the ECG signal. Instead, some embodiments of thepresent disclosure include a patch 2266 that processes a subset oflayers 2268, and outputs the first subset of layers to a server 2270that performs the remaining layers 2272 of the neural network.Advantageously, the data being transmitted can be outputs of a layer ina neural network, which provide for enhanced data security andencryption.

Some embodiments improve on security of health data. For example, if theentire ECG signal, R peaks, RR intervals, or a final determination ofthe likelihood of AFib was transmitted from the wearable device to anexternal computing system, a third party may intercept such data anddiscern sensitive health data of a user. Instead, some embodimentsdisclosed herein transmit output of an intermediary layer of the neuralnetwork (such as the output of the encoder) to be processed bysubsequent layers externally (such as the decoder). The transmitted datacan be output of a layer in a neural network, which inherently providesencryption of data for data transmission. Thus, even if a third partywere to intercept such data, they would not be able to decode the data,nor reverse engineer the original input of the neural network.

In some embodiments, the first subset of layers can include lesscomputationally heavy layers whereas the second subset of layers caninclude more computationally heavy layers. Advantageously, the wearabledevice can process the less computationally heavy layers, and theexternal computing systems that are agnostic to the processing of thewearable device can process the more computationally heavy layers (suchas in servers with much larger processing power and memory capacity).

In some embodiments, the wearable patch can perform preprocessing on thedetected cardiac signals before processing the data through the firstsubset of layers of the neural network. For example, the wearable patchcan perform preprocessing such as downsampling, normalizing, digitalfiltering, and/or the like. Moreover, the wearable patch can performmore complex preprocessing, such as a discrete wavelet transform,continuous wavelet transform, discrete Fourier transform and discretecosine transform. These transforms can be beneficial in lossycompression schemes because the transformations transform the data in away that results in a lot of values in the data being very close tozero. An encoding can then be created to simply store the values withthe highest amplitude, which would limit error when reconstructing thesignal.

The wearable patch may apply a discrete wavelet transform or a discretecosine transform, and simply apply algorithms to make inferences on thedetected cardiac data. However, such approaches may fail to capture moresubtle features that are critical for the end application, such as ECGrhythm classification, because higher amplitude features are favored. Insome embodiments, the system can train a neural network encoder on thistransformed set of data (e.g., cardiac data that has been preprocessed)in order to more intelligently encode the data needed for the endapplication. Advantageously, this may be beneficial for someapplications, because a transform may already be performing certainactions to the signal needed for the end application, which could leadto a more powerful or simpler neural network encoder design that hadfewer parameters compared to one that works off the raw signal.

In some embodiments, the neural network can be trained and/or designedto transmit an optimal data dimensionality from the wearable device tothe external computing system. FIG. 22E is a schematic diagram of anembodiment of designing (and/or training) the neural network. The patch2281 can process a first subset of layers 2284A, 2286A, 2288A of a firstneural network 2284, a second neural network 2286, and/or a third neuralnetwork 2288. The patch 2281 can transmit the output of the first subsetof layers to a server 2282. The server can process a second subset oflayers 2284B, 2286B, 2288B of the first neural network 2284, the secondneural network 2286, and/or the third neural network 2288.

The neural network can be trained and/or designed based on one or morefactors, such as available processing power and/or memory of thewearable device (for the purposes of the disclosure, the neural networkswill be described as being trained, but the features can be applied fordesigning or processing data through the neural network, and viceversa). For example, if the wearable device has less processing power,the neural network can be trained to perform less computation and/orperform processing through less layers of the neural network on thewearable device, and process more on the external computing system side.The patch 2281 can apply the third neural network 2288 that includesfewer computations and/or layers of the neural network.

In some embodiments, the neural network can be trained according tonetwork availability between the wearable device and the computingsystem. For example, the neural network can be trained to process morelayers and/or transmit a smaller data dimensionality from the wearabledevice to the external computing system when the network throughput islower. The patch 2281 can apply the second neural network 2286 thatincludes a smaller data dimensionality being transferred to the server2282 for when the network connection is weak (or below a threshold).

In some embodiments, the neural network can be trained according toavailable battery life of the wearable device. For example, the neuralnetwork can be trained to process less layers on the wearable devicewhen the battery life is lower. The patch 2281 can apply the thirdneural network 2288 that includes fewer computations and/or layers ofthe neural network.

In some embodiments, the neural network can be trained according to thedesired outcome. For example, the patch 2281 can apply the first neuralnetwork 2284 that includes more outputs of the neural network, such asmore characteristics of the user. For example, if the patient is incritical care or has an indication of AFib, the patch 2281 can apply thefirst neural network 2284 and process more layers and nodes through thefirst subset of layers 2284A, such that the server 2282 can output moreinformation via the outputs of the second subset of layers 2284B.

In some embodiments, the software in the patch 2281 can includeinstructions for a particular neural network designed (and/or trained)for a particular application. For example, a neural network can beselected for a patch 2281 with certain technical constraints (e.g.,memory or processor), and to serve a particular purpose (e.g., predictAFib), whereas another neural network can be selected for a differentpatch with different characteristics and for different purposes. Forexample, an accurate detection of AFib can require more data from thepatch than another desired purpose. Thus, a patch that is to detect AFibmay have software that computes more layers than another patch.

In some embodiments, the software in the patch 2281 can be designed(and/or trained) based on performance metrics. For example, the softwarecan select a neural network that is designed to produce a certainaccuracy in detection and/or a certain false alarm rate.

In some embodiments, the software in the patch 2281 can include aplurality of neural networks. The patch 2281 can select a particularneural network out of the plurality of networks. For example, theselection can be based on a neural network designed for low batterypower or network connectivity (e.g., switching to a neural network thatprocesses less on the patch or transmits less data through the network).

In some embodiments, the neural network can be trained to recreate theoriginal input signal to the neural network, such as the ECG signal, RRinterval information, R peak information, a metric derived from the ECGsignal, accelerometer data, impedance, temperature, audio (e.g.,snoring), ambient light, and/or the like. The patch 2281 can transmitdata of a smaller data dimension to server 2282 for the server 2282 torecreate the original input signal, or a derivative thereof, such as theoriginal input signal sampled at a lower frequency, lower qualitysignal, signal with a certain threshold for reconstruction error, and/orthe like.

In some embodiments, the neural network can be trained according to thecurrent activity of the user. FIG. 22F is a schematic diagram of anembodiment for designing, training, and/or selecting a neural networkbased on a current activity of the user. For example, the user can besleeping 2296 or running 2292, or subject to certain medical treatmentthat may require different types of peak sensitivities. There arecertain activities that may require higher precision or accuracy to beable to effectively detect heart beat irregularities. If the activityrequires higher R peak accuracy (e.g., for a user who is sleeping 2296,and signal levels 2298 are less affected by motion artifacts—and thuscan rely more confidently on the ECG signal—than a user who is running2292, so a higher R peak accuracy is preferred for the running user 2292for a more accurate prediction. For the running user 2292, a more robustencoding process may be used to ensure data accuracy), neural networkcan be trained such that the wearable device may transmit data of higherresolution. In contrast, for a user who is running 2292, the R signallevels 2294 may be clear (e.g., clearly show R peaks), and thus, a mayrequire lower R peak sensitivity. In this scenario, the patch can selecta neural network that is designed to not require higher R peaksensitivity, which can lead to advantages described herein, such asreduced processing power or network throughput.

Training of models, such as artificial intelligence models isnecessarily rooted in computer technology, and improves on models byusing training data to train such models and thereafter applying themodels to a new set of user data. Such training involves complexprocessing that typically requires a lot of processor computing andextended periods of time with large training data sets, which aretypically performed by massive server systems. Training of models canrequire logistic regression or forward/backward propagating of trainingdata that can include input data and expected output values that areused to adjust parameters of the models. Such training is the frameworkof machine learning algorithms that enable the models to be applied tonew data (such as new biometric data) and make predictions that themodel was trained for based on the weights or scores that were adjustedduring training. Such training reduces false positives and increases theperformance of detection of AFib.

In some embodiments, the wearable device can store a plurality of neuralnetworks and can apply a neural network based on one or morecharacteristics, such as characteristics of the wearable device,network, current activity of the user, etc. For example, the wearabledevice can process a first neural network if the battery life is low, ora second neural network if the network throughput is high.

In some embodiments, the wearable device can process the ECG signalcontinuously through the first subset of layers of the neural network asthe wearable device detects the ECG signal in real time. The wearabledevice can transmit the output of the first subset of layerscontinuously to the external computing device, and the externalcomputing device can process the received output through the secondsubset of layers of the neural network in order to derive acharacteristic of the user (e.g., cardiac arrhythmia) in substantiallyreal-time. Advantageously, the external computing device can identifycardiac arrhythmia in substantial real-time of occurring with the userwhile reducing network throughput, memory requirements, and processingpower requirements. Moreover, more powerful algorithms and neuralnetworks, which would not have been able to be performed on the wearabledevice itself due to device restrictions nor completely on externalcomputing systems due to input requirements, are now feasible.

In some embodiments, the neural network can include a first neuralnetwork and a second neural network. The first neural network (e.g., atemporally distributed feature extraction model) can receive as inputsone or more types of data, such as the ECG signal of a user, R peakdata, RR interval sub-sequences (e.g., RR intervals in 30 second slidingwindows), encoded features, and/or the like as further described herein.The first neural network can process the inputted RR intervalsub-sequences and generate Atrial Fibrillation (AF) features for eachsliding window. The output of the first neural network can be fed intothe second neural network including recurrent layers with Long ShortTerm Memory (LSTM) cells. The LSTM cells can process the AF features ofthe current sliding window and past sliding windows to process the datausing a transfer learning approach, where over the course of multiplesliding windows, the LSTM can advantageously predict an indication of acardiac arrhythmia that takes into account data longer than the 30second window.

In some embodiments, the first neural network and the second neuralnetwork is trained in stages. The first neural network can be trainedfirst. Training data of 30 second intervals can be fed into the firstneural network, and the weights can be adjusted based on the expectedoutput of the first neural network. After the first neural network istrained, the weights of the first neural network can be frozen, and thesecond neural network can be trained. The second neural network can betrained based on longer intervals of data that are fed into the firstneural network in 30 second windows, and the outputs are fed into thesecond neural network with LSTM cells. The second neural network canmake predictions of AFib over the longer data sets, and the predictedoutputs can be used to adjust the weights of the second neural network.Once the second neural network is trained, the weights of the firstneural networks can be unfrozen, and both the first and second neuralnetworks can be trained simultaneously using further training data.

As wearable bio-signal processing applications grow, there is anincreasing need to improve the sophistication and utility of theseapplications. A common scenario is to have a small, battery-poweredwearable, with an array of sensors measuring bio-signals processed and,optionally, transmitted in real-time or near real-time. Since devicesize will affect comfort, which in turn influences compliance and thususefulness, it is advantageous to have as small of a device as possible.This introduces several constraints to the system—mainly battery lifeand processing capability—which in turn can negatively affect userexperience by requiring the user to charge the battery or resulting inlower quality analytics and lower compliance. These constraintstypically limit the sophistication of processing that occurs on thedevice, so an option utilized by many applications is to offloadprocessing to systems with fewer constraints, such as smartphones, cloudservers or base stations.

Within this paradigm, a trade-off exists between the amount and type ofdata offloaded for additional processing. Ideally, the full-fidelity,raw bio-signals are offloaded in real-time to maximize processingsophistication on the lesser constrained part of the system.Unfortunately, within the limits of current technology, this istypically infeasible due to the battery cost and technical limitationsof transmitting full-fidelity signals in real-time or even timelymanner. A way to overcome this limitation is to send some smallerrepresentation of the signal, thus minimizing power requirements andmaximizing the time between battery charging or replacement, or thepotential monitoring duration of a device if powered by a single batterycharge.

There exist many strategies for creating this alternativerepresentation. Depending on the application, custom-designed algorithmscan be used to select features uploaded for further processing. Anexample of this strategy is a Mobile Cardiac Telemetry (MCT) device, ora wearable activity tracker capable of detecting and uploading thelocation of heart beats derived by algorithms running on the device,which could then be further analyzed to detect heart arrhythmias by moresophisticated algorithms on a cloud server. Another example is awearable device that uses accelerometry to analyze gait, assess activitylevels, and the risk of falls by uploading the results of the on-devicestep counting algorithm. The limitation with this set of approaches isthat the selected feature may not be optimal for the end application,and the device-side algorithms may also be more computationallyburdensome to compute than needed.

Another common approach would be to compress the raw bio-signal data ina way that allows reconstruction of the signal. Commonly used techniquesinclude lossless and lossy compression schemes. Lossless compression,while allowing for optimal processing on cloud-based servers, does nottypically provide a high enough compression ratio to reduce the datatransmission burden sufficiently to be viable. Lossy compressiontechniques, such as wavelet-based compression, can achieve a high levelof compression relative to the amount of error introduced duringreconstruction. While this strategy may work well for certainapplications, there is no guarantee that the reconstructed signal willkeep the necessary information for the end application, since theirobjective is only to minimize signal reconstruction error. This type ofobjective is driven primarily by signal features with a high amplitude,and may ignore or distort features with small amplitude characteristics.Depending on the application, signal features that have relatively smallamplitude may be critical to the end objective, such as with the subtleP-waves in an ECG associated with atrial depolarization, critical todetected rhythms such as atrial fibrillation and complete heart block.

System and methods disclosed herein achieves an optimal balance for allelements in a system: device processing burden, system transmissionburden, and end application utility. This is accomplished utilizingneural network encoding, which can be used to generate a scheme thatcompresses raw bio-signal data in a computational friendly manner,preserving information needed for a specific end application. A neuralnetwork can encode a signal into a compressed format, and thenreconstruct the signal. More generically, a neural network architecturecan be built into encoder and decoder sections.

For this application, the encoder section would be responsible forcompressing the raw bio-signals, would run on the target device, andwould result in an output that has a smaller dimensionality than theinput.

The decoder section would be responsible for producing predictions for atarget application, starting from the encoded layer of the neuralnetwork, and would be employed elsewhere than the target device, forexample, on a smartphone, cloud server, or communication hub/gateway.The encoder and decoder portions are trained as one end-to-end neuralnetwork, this would effectively create an optimal encoder and a decodersimultaneously because the encoder and decoder sections are beingoptimized together for the same objective. This method maximizes theflexibility of the system design, which can lead to the most optimalimplementation. For example, the encoder section can be designed totarget a specific computational complexity and compression, and theneural network output can be selected to be exactly what is needed forthe end application. This technique is ideal for any real-time and nearreal-time signal processing applications that require high computationalcomplexity and have a mechanism to offload processing.

Devices that utilize electrocardiogram (ECG), photoplethysmogram (PPG)or audio signals to detect both electrical and structural heartconditions in real- or near-real time face challenges outlined above.Certain heart arrhythmias present themselves in subtle ways, forexample, in low-amplitude P-wave patterns in ECG recordings, oftenrequiring a high level of sophistication and sensitivity in detectionalgorithms. To maximize clinical utility, rhythms may be categorized inup to fifteen different classes including noise, which not only rely onbeat-to-beat heart rate patterns but also the differences in morphologythat can often be elusive, especially in single-lead ECG applications.Existing devices may simply offload the entire raw signal, necessitatingfrequent charging or battery replacement, or use compression techniquesto offload processing, or detect commonly understood features of the ECGsignal on the device, and upload those to a separate system for furtherprocessing. The features may include the location of ECG morphologicalfeatures such as QRS complexes, P waves, etc. Due to the limitedcomputation power and battery life of these devices, algorithms willhave limited accuracy in their ability to detect these morphologicalfeatures. Neural network encoding can be employed to transmit acompressed representation of the ECG signal. This representation can beoptimized for the specific end application, such as identifying specificcardiac arrhythmias, identifying ECG morphological features such as QRScomplexes, classifying beat types, determining heart rate, etc.

In an example implementation of this strategy for real- or nearreal-time arrhythmia detection from ECG recordings, a neural networkcould first be trained using a set of 30 s ECG strips, sampled at 200Hz, for example, with 14 distinct rhythm labels for each sample. As apreprocessing step, the 200 Hz signal will be down sampled by a factorof 3 using a moving average filter, and then conditioned using an IIRhigh pass filter with a 2 Hz cutoff. This input is also scaled to haveunit variance. This will leave information in the range of ˜33 Hz, whichwill contain most of the ECG morphological information. This makes theinput dimension of the signal into the neural network 2000×1. Asmentioned above, the goal of the first section of the Neural Network isto compress the signal, so it will need to reduce the dimension of theinput signal.

In an example embodiment, the encoding section of the neural networkwould have a sequence of convolutions and pooling layers, where eachpooling layer reduces the temporal dimension by a factor of 2. With asequence of convolution and pooling layers that repeats 3 times, thiswould result in a temporal dimension reduced by a factor of 8. Also inthis example embodiment, the final convolution layer of the encodersection would have 2 filters, which will make the output dimension 250×2and is referred to as the encoded output.

The next layers of the network employ a strategy to return the temporaldimension size back to its original size at the network input. Thiscould be accomplished using several transposed convolution layers(sometimes referred to as deconvolution layers). The layers of thisnetwork up to this point can, in one embodiment, be pretrained topredict the original input signal. In one embodiment, the remaininglayers consist of residual convolutional filter blocks. In this example,12 sequential blocks were used, with each block containing 2convolutional layers, with a residual connection from the input tooutput. These blocks also reduce the temporal dimension by a factor of 2every other block. After the last of these blocks, a time-distributeddense layer is evaluated at each temporal point to predict a uniquerhythm class. The above example describes one embodiment of a frameworkfor training an algorithm to encode ECG information in a way thatpreserves diagnostic information in a compressed format.

In some embodiments, the encoding process can be implemented toperpetually generate ECG features. Using the above example neuralnetwork, a 6000-sample length ECG strip will create 250 examples of afeature vector with two dimensions. This can be thought of as every 24individual ECG samples resulting in two encoded features. Since thenature of this processing is causal, these features can be createdperpetually, without any concept of 30 s ECG segments. These featurescan be scaled to be stored in a compact integer representation, andlossless compression can be applied to further reduce the featuresmemory footprint. With a continuous stream of encoded features, aserver-side algorithm may employ a number of different strategies todetect events of interest using the encoded features.

In one embodiment for detecting segments that contain an arrhythmia(such as CHB, VT, AFIB/AFL, SVT, AVB etc), the original multiclassalgorithm used to train the ECG encoding can be evaluated using30-second contexts of encoded features that are generated on the patch.This algorithm can be evaluated on a rolling window of 30-secondcontexts, with or without overlap. Segments that have a high probabilityof containing an arrhythmia can be used to determine if an event ofinterest is present.

In one embodiment, the encoding section of the neural network may use acombination of convolutional, pooling and recurrent layers such as LSTMto reduce the dimensionality of the signal.

In one embodiment for detecting VT events, a neural network can betrained using the encoded features to predict sequences of VT in ECGsegments that are about 1 second in duration. The benefit of this isthat differentiation between more or less severe segments can be madefor determining which events are of more interest. For example, longersegments could be prioritized over shorter ones. This algorithm could beused to determine the burden of VT episodes that meet a more specificlength criterion. Traditional approaches to VT detection—particularly ona battery-constrained embedded device—relies on beat patterns thatexhibit sudden increase in rate.

The reliance on beat detection, for example, the widely usedPan-Tompkins algorithm, whose performance is often poor on wider QRSbeats, inevitably limits the ability to reliably discern beat patternsin arrhythmia such as VT. Even when full ECG is present and morphologyinformation can be studied via signal processing techniques, thecomputational burden is high on a battery-operated device, leading todevices requiring charging once or more times daily and/or batteryreplacement on both the sensor and the gateway or mobile phone unit. Forexample, many current Mobile Cardiac Telemetry (MCT) modalities sufferfrom this requirement, which transfers its burden upon the patient,therefore contributing to lower compliance and lower analyzable time andclinical value from the diagnostic device.

In one embodiment for evaluating AF burden, the original neural networktrained to create the ECG encoding can be evaluated over 30-secondcontexts to determine if the segment contains Atrial Fibrillation (AFIB)or Atrial Flutter (AFL), and the summation of these times can be used tocalculate burden. In addition for Atrial Flutter, existing modalitiesoften struggle with the differentiation of AFL from other regularrhythms such as Normal Sinus, since the irregularly irregular beatpattern characteristic of Atrial Fibrillation is not present. Thisrequires closer examination of the signal between beats, which theencoded features allow. Of course, full ECG allows for this as well, butwould require more computational power to process the information, whichoften takes the form of frequency domain analysis and in turn also leadsto a less patient-friendly device with charging or battery replacementsas discussed with in the MCTs embodiments above.

In another embodiment for evaluating AF burden, the encoded features canbe fed into a recurrent neural network over a larger context, forexample 30 minutes, to predict AF burden in the same manner as describedabove.

In another embodiment for characterizing VT, the encoded features can befed into a network over a larger context, for example 30 minutes, topredict the total duration of VT and/or the number of discrete VTepisodes that are present.

In one embodiment for determining heart rate, a neural network could betrained to predict the number of heart beats contained in a segment oftime, for example, 3 seconds, using the encoded features. This neuralnetwork can be used to predict the number of beats in a segment of timeover a sliding window, which can then be used to estimate heart rate atvarious times. This heart rate information can be used for furtherrefinement or prioritization of events of interests that are detectedusing other algorithms. A similar strategy would enable the estimationof heart rate variability in lieu of or in addition to heart rate.

In one embodiment for determining ectopic beats counts, a neural networkcould be trained to predict the number of ectopic beats contained in asegment of time, for example, 3 seconds, using the encoded features.This neural network can be used to estimate the total number of ectopicbeats over a window of time, for example, in a 1—, 12—or 24—hour period.Alternatively, combined with the heart beat embodiment described above,this embodiment could be used to estimate the ratio of ectopic to normalbeats, for example, expressed as a percentage over a 1—, 12—or 24—hourperiod. This embodiment, described agnostically above, could be appliedto either ventricular or supraventricular ectopic beats, and/or tocouplets and triplets of each.

In one embodiment, R-peak locations may be approximated from thereconstructed time series from the encoded features. These R-peaklocations could, on their own, be used to calculate heart rate, augmentthe accuracy of heart rate estimation from an alternate method, or todetect cardiac Pause events defined as a clinically significant gap intime between two ventricular contractions, for example, greater thanthree seconds. Like heart rates, this information can be used to furtherfilter or prioritize events of interests whose ECG is to be transmittedand reported. In another embodiment, the reconstructed signal itself canbe viewed along with portions of full ECG to provide more context, forexample, whether low, high, or irregular heart rate continues from theend of the ECG segment. This contextual information can aid in theprocess of manually requesting more full resolution ECG from the patchbased on visual inspection, leading to an overall higher diagnosticacuity backed by human quality assurance.

In an alternate embodiment, heart rates based on these R-peak locationscould be plotted alongside the selected ECG strips in a clinical reportin a manner that provides greater temporal context for the event shownin the brief (e.g., 8- or 16-second strip). The heart rates could beplotted as either instantaneous rates (e.g., based on the intervalbetween two R-peaks), or as a heart rate trend over time with each datapoint derived from multiple R-peaks. An alternative embodiment couldplot the average, max and min heart rates trends simultaneously toprovide even greater clinical context.

In some embodiments, a wearable device that utilizes PPG andaccelerometry can be used to detect Atrial Fibrillation (AFIB) episodes,and to estimate AFIB burden levels. Sensors in this configuration verycommonly suffer from degraded signal quality due to environmentalfactors, such as being worn improperly, motion of the device, or poorphysiological conditions. For this reason, it is advantageous to utilizethe full fidelity raw sensor readings in order to detect AFIB orestimate AFIB burden, since the noise can be understood and preventalgorithms from predicting false positives.

Existing strategies may detect heart rate intervals on the device, anduse this information to infer AFIB episodes. Signal quality metrics maybe separately computed and uploaded. This strategy will suffer whenperiods of poor signal quality create false heart rate intervalreadings. Using the strategy outlined above, a neural network could bedeveloped to predict AFIB episodes or AFIB burden from the rawPPG/accelerometer signals, which also encodes the signal in a compressedrepresentation. This network can learn to encode the information in away that is least prone to false predictions due to poor signal quality.The encoding can be run on the device, and the encoded data can beuploaded so that a decoding section would be evaluated on more powerfulservers. In addition to identifying AFIB episodes and AFIB burden,encoded sensor data from a wearable PPG device could be used tocharacterize pulse rate or detect other heart arrhythmias, such asventricular tachycardia, pause, atrioventricular block etc. They canalso be used to detect health conditions such as diabetes, hypertension,depression, congestive heart failure, sleep apnea etc.

In another embodiment, encoded features can be optimized to describewaveform components of ECG beats in addition to R peaks, e.g. P-waves,T-waves and/or segments between points in a waveform. A neural networkcan be trained on the encoded features to estimate PR intervals, whosepatterns can discern between subtle categorization of AV Block (1^(st)degree, 2^(nd) degree Mobitz I I.e. Wenckebach, 2^(nd) degree Mobitz II)or characterize Wolff-Parkinson-White syndrome along with inference ofQRS width. Some of these less life-threatening, asymptomatic arrhythmiasmay be desirable to be transmitted, especially if the device does notrecord and store all signal continuously like event recorders and ILRsor some MCTs where not all of the signal is fully analyzed.

In a similar manner, a neural network can be trained on the encodedfeatures to estimate QT intervals, which can uncover clinicallysignificant findings such as Long QT Syndrome, which may be monitoredover the course of the wear time. For example, this QT measurementapproach could be used for drug titration, where a patient would adjustdrug dosage to determine the optimal mix of drug efficacy and safety onan individualized basis. A neural network can also be trained to detectchanges in ST segments, which can be monitored to look for ST elevationor depression which may be warning signs for myocardial infarction.

In another embodiment, the encoding method can be dynamically adjustedand adapted as the system receives and learns more about thecharacteristics of the signal. It can also be swapped with a differentscheme if the target rhythm changes for a specific patient. Forinstance, instead of a generalized method of detecting arrhythmia types,for certain neurology applications like post-stroke monitoring, theprimary focus may be to detect AF, and regardless of heart rate orduration of the episode. A pair of encoding and decoding networkspretrained and optimized for high sensitivity of AF may be used in thiscase. These different encoders can be learned offline and updated overthe air to the firmware when change is necessary, or pre-programmed asan option on the embedded device to be activated when requested.Alternate embodiments might implement algorithms optimized for eithersensitivity or specificity performance, in a similar manner.

In certain embodiments, the input signal may include channels fromaccelerometer and/or gyroscope axes, which can often take up a lot ofspace due to the number of axes multiplied by the required sampling rate(e.g. 20 Hz to enable inference of step counts). The features in 3-axisaccelerometer and/or 3-axis gyroscope signal can be reduced to a smallerdimensionality, which may contain valuable information not just limitedto pre-defined physical metrics like magnitude. In fact, characteristicsthat can only be obtained via higher frequency sampling could be encodedto enable finer distinction between activities. Encoded accelerometerand/or gyroscope features on their own can be used to determine sleepstages and activity levels, step counts, orientation, activity type,etc. Encoded accelerometer and/or gyroscope features on their own, orfeatures from these signals encoded together with ECG, can be used tofurther differentiate between signal generated by the physiologicalevents such as arrhythmia versus that of motion artifact. The same datais also helpful as one input (trended activity level) to evaluating thehealth of heart failure patients, for example, in the form of anambulatory monitor of decompensation events after hospital discharge.

In other embodiments, impedance information, or some other measurementindicative of skin contact such as galvanic skin response may be encodedalong with the ECG or independently. A neural network can be trained todetect areas of leads off, and in conjunction with ECG features, be usedto filter out segments of unreliable signal. Since the same impedancevalues can mean different levels of signal quality on different patientsand conditions, a simple state-machine type of algorithm on impedancevalues alone may result in leads-on conditions when signal is actuallynon-physiological, or on the converse, leads off detected when thesignal is actually ECG and therefore analyzable. The former isparticularly an issue for ambulatory devices that are prone to noisewhich can sometimes mimic arrhythmia and therefore result in falsepositive transmissions, though some of it can be mitigated throughhardware design and flexible materials such as on a patch form factor.Decoder algorithms that use encoded features of ECG, together with rawor encoded features of secondary signals such as impedance oraccelerometer can further eliminate these false positives, while alsoensuring that real ECG signal is not lost due to poor impedance.

In certain embodiments, suspected arrhythmia detected using the encodedfeatures may benefit from further analysis using the full-resolution ECGon the original device. The power savings enabled by an encoderimplementation would allow a decoder-based algorithm to request eithertransmission or on-board analysis of portions of full-resolution ECGstored on the device. A separate algorithm, which may be derived fromconvolutional neural networks trained on labeled full-resolution ECG,running on restricted regions of the original ECG would becomputationally feasible.

In certain embodiments, encoded features may be used to learn andpredict arrhythmia such as Atrial Fibrillation or other healthconditions that may occur in the near future, enabling earlyintervention.

In certain embodiments, the processing, either the encoding or thesubsequent layers that use the encoded features (decoding), may be doneon the intermediate device transmitted prior to long-range connectionsuch as cell or satellite, such as a smartphone or a gateway device. Incertain use cases, for example, where raw signal does not need to bewritten to device memory, which is often an heavy part of batteryconsumption in these limited power modalities, both encoding anddecoding can occur on the device itself, such as a patch, smartwatch, orother wearables capable of continuously monitoring ECG or other signals,and only the encoded signal written. This approach could be useful in ascenario where storage space is limited, but algorithms may need to runon data stored in memory (e.g., data for ECG that occurred in the past).

In certain embodiments, the technique may be applied to other surface ortranscutaneous bio-signals other than ECG, such as PPG from the wrist,ear lobe or chest. Wearable devices, often consumer-grade, feature PPGsignal capture to enable heart rate trending and in some cases,screening for Atrial Fibrillation. These health insight capabilities areoften limited to detection of highly limited duration, e.g. 30 seconds,and require relatively high-power optical sensing, e.g. with green lightto reliably capture waveforms through various skin types and motion. Theentirety of the PPG cannot be stored or transmitted for more richcontextual analysis of anything beyond simple beat detection in case PPGwave morphology contains signatures unique to arrhythmic blood flow, orto aggregate into more long-term and clinically useful metrics such asAF burden.

In some embodiments, PPG and/or ECG waveforms are encoded in order toinfer blood pressure and other cardiac metrics such as blood perfusion,blood volume, cardiac output, ejection fraction, valve function and/orhealth, etc., where the traditionally computed beat-to-beat or evenfiducial point intervals may not be the most useful features. Althoughcertain embodiments are described with PPG, it is understood that ECGcan also be used where applicable, and vice versa.

In certain embodiments, the technique may be applied to even more dataheavy signal such as EEG. Wearable applications employing EEGs are oftenlimited to laboratory settings due to the number of channels and bulk ofdevices. Encoded features trained of EEG could enable offloading ofhigh-computational power decoder algorithms to a server and enablemobility of devices that for example, translate brain activity intocommunication.

In some embodiments, the neural network encoding may be implemented indedicated hardware, such as an FPGA IC to further reduce batteryconsumption on the target device. The neural network encoder may befurther optimized for an embedded application by utilizing integer mathor binary operations.

In some embodiments, image or video streams that result in detection ofmarkers or objects or physiological measurements can use neural networkencoding to offload processing to more powerful systems. Exampleapplications would be

object detection in vehicles, security surveillance systems

emotional response from facial video

physiological measurements such as heart rate, breathing based on videostreams of face or chest

detection of seizures

detection of falls

augmented reality

infant monitoring of notable events (crying, breathing issues)

In some embodiments, audio data may be encoded for the purpose ofidentifying speech events or translating speech in real-time,respiration monitoring, diagnosis of respiratory illnesses, detection ofsnoring or apnea.

In some embodiments, sonography data may be encoded for the purpose foridentifying contraction during pregnancy, detecting and characterizingfetal heart beats, identifying anomalous physiology such as tumors,strokes, blocked arteries, infection, etc.

Computing Systems and Methods

In some embodiments, the systems, tools and methods of using samedescribed above enable interactivity and data collection performed by acomputing system 13000. FIG. 23 is a block diagram showing an embodimentin which the computing system 13000 is in communication with a network13002 and various external computing systems 13004, such as a wearablesystem 13005, a gateway device 13006, which are also in communicationwith the network 13002. The computing system 13000 may be used toimplement systems and methods described herein. While the externalsystem 13004 are shown as grouped it is recognized that each of thesystems may be external from each other and/or remotely located.

In some embodiments, the computing system 13000 includes one or morecomputing devices, for example, a server, a laptop computer, a mobiledevice (for example, smart phone, smart watch, tablet, personal digitalassistant), a kiosk, automobile console, or a media player, for example.In one embodiment, the computing device 13000 includes one or morecentral processing units (CPUs) 13105, which may each include aconventional or proprietary microprocessor. The computing device 13000further includes one or more memory 13130, such as random access memory(RAM) for temporary storage of information, one or more read only memory(ROM) for permanent storage of information, and one or more mass storagedevice 13120, such as a hard drive, diskette, solid state drive, oroptical media storage device. In certain embodiments, the processingdevice, cloud server, server or gateway device, may be implemented as acomputing system 1300. In one embodiment, the modules of the computingsystem 13000 are connected to the computer using a standard based bussystem. In different embodiments, the standard based bus system could beimplemented in Peripheral Component Interconnect (PCI), Microchannel,Small Computer computing system Interface (SCSI), Industrial StandardArchitecture (ISA) and Extended ISA (EISA) architectures, for example.In addition, the functionality provided for in the components andmodules of computing device 13000 may be combined into fewer componentsand modules or further separated into additional components and modules.

The computing device 13000 may be controlled and coordinated byoperating system software, for example, iOS, Windows XP, Windows Vista,Windows 7, Windows 8, Windows 10, Windows Server, Embedded Windows,Unix, Linux, Ubuntu Linux, SunOS, Solaris, Blackberry OS, Android, orother operating systems. In Macintosh systems, the operating system maybe any available operating system, such as MAC OS X. In otherembodiments, the computing device 13000 may be controlled by aproprietary operating system. Conventional operating systems control andschedule computer processes for execution, perform memory management,provide file system, networking, I/O services, and provide a userinterface, such as a graphical user interface (GUI), among other things.

The exemplary computing device 13000 may include one or more I/Ointerfaces and devices 13110, for example, a touchpad or touchscreen,but could also include a keyboard, mouse, and printer. In oneembodiment, the I/O interfaces and devices 13110 include one or moredisplay devices (such as a touchscreen or monitor) that allow visualpresentation of data to a user. More particularly, a display device mayprovide for the presentation of GUIs, application software data, andmultimedia presentations, for example. The computing system 13000 mayalso include one or more multimedia devices 13140, such as cameras,speakers, video cards, graphics accelerators, and microphones, forexample.

The I/O interfaces and devices 13110, in one embodiment of the computingsystem and application tools, may provide a communication interface tovarious external devices. In one embodiment, the computing device 13000is electronically coupled to a network 13002, which comprises one ormore of a local area network, a wide area network, and/or the Internet,for example, via a wired, wireless, or combination of wired andwireless, communication link 13115. The network 13002 can communicatewith various sensors, computing devices, and/or other electronic devicesvia wired or wireless communication links.

In some embodiments, the filter criteria, signals and data are processedby rhythm inference module an application tool according to the methodsand systems described herein, may be provided to the computing system13000 over the network 13002 from one or more data sources 13010. Thedata sources may include one or more internal and/or external databases,data sources, and physical data stores. The data sources 13010, externalcomputing systems 13004 and the rhythm interface module 13190 mayinclude databases for storing data (for example, feature data, rawsignal data, patient data) according to the systems and methodsdescribed above, databases for storing data that has been processed (forexample, data to be transmitted to the sensor, data to be sent to theclinician) according to the systems and methods described above. In oneembodiment of FIG. 24, the sensor data 14050 may, in some embodiments,store data received from the sensor, received from the clinician, and soforth. The Rules Database 14060 may, in some embodiments, store data(for example, instructions, preferences, profile) that establishparameters for the thresholds for analyzing the feature data. In someembodiments, one or more of the databases or data sources may beimplemented using a relational database, such as Sybase, Oracle,CodeBase, MySQL, SQLite, and Microsoft® SQL Server, and other types ofdatabases such as, for example, a flat file database, anentity-relationship database, and object-oriented database, NoSQLdatabase, and/or a record-based database.

The computing system, in one embodiment, includes a rhythm interfacemodule 13190 that may be stored in the mass storage device 13120 asexecutable software codes that are executed by the CPU 13105. The rhythminterface module 13190 may have a Feature Module 14010, an AlternateData Module 14020, an Inference Module 14030, a Feedback Module 14040, aSensor Data Database 14050, and a Rules Database 14060. These modulesmay include by way of example, components, such as software components,object-oriented software components, subroutines, segments of programcode, drivers, firmware, microcode, circuitry, data, databases, datastructures, tables, arrays, and variables. These modules are alsoconfigured to perform the processes disclosed herein including, in someembodiments, the processes described with respect to FIGS. 10 to 17.

Lossless Compression and/or Quantization

In some embodiments of the present disclosure, the system can performlossless compression on the output of the first set of layers in aneural network before transmitting the data to a remote server toprocess the data through the second set of layers in a neural network.The lossless compression algorithms may allow the original data to beperfectly reconstructed from compressed data. In some embodiments, thesystem can perform compression where the remote server can reconstructthe compressed data with an accuracy of a certain threshold, thusreducing loss.

FIG. 25 illustrates an example of an implementation of losslesscompression with the ECG encoder. The patch 2226 can receive ECG data2232 of a user. The monitor (e.g., the patch 2226) can encode the datathrough the first set of layers 2222 of the neural network (e.g., viathe ECG Encoder 2230) and transmit the encoded output to a losslesscompressor 2502. The lossless compressor 2502 can compress the data toan even smaller dimensionality to be transmitted to a remote server2228. The remote server 2228 can decompress the data via the lossesdecompressor 2504. The remote server 2228 can then process thedecompressed data through the second subset of layers 2224 of the neuralnetwork via the classifier 2238.

In one example, a lossless compression scheme can be used to map betweenoutput encoded features and a shorter length representation. For an 8bit integer, a value of 0 as a binary representation “00000000” can bethe outputted encoded feature of the first subset of layers of theneural network from the ECG encoder 2230, but the lossless compressionscheme can change the “00000000” 8 bit number representation tosomething else. The lossless compression scheme can have a mappingbetween values that are outputted from the ECG encoder 2230 to acompressed value. For example, the “0000000” 8 bit number representationfrom the ECG encoder 2230 can be represented by “10,” which only takes 2bits instead of 8. The lossless compression scheme can use shorter bitlength codes for more common output encoded features, whereas longer bitlength codes are used for more rare encoded features. Advantageously,less data is transmitted to the remote server. For example, if there aremuch more 0 values outputting from the ECG encoder 2230, the losslesscompression scheme can map a 0 value to “10”. However, if a value of 433is rarely outputted from the ECG encoder 2230, the lossless compressionscheme can map a 433 value with a binary representation of “110110001”to “5523”.

In some embodiments, the lossless decompressor 2504 can retrieve mappedvalues that are predetermined. In some embodiments, the losslessdecompressor 2504 can retrieve mapped values that are static and do notchange. The lossless decompressor 2504 can receive the mapped valuesfrom the lossless compressor 2502. The remote server 2228 can map thecompressed data (e.g., the mapped values) to actual values thatcorrespond to the ECG encoder 2230. For example, if the losslessdecompressor 2504 receives a “10” value and a “5523” value, the losslessdecompressor 2504 can map these values to “0” and “443” and output thesevalues accordingly. These values can then be processed through thesecond subset of layers in the neural network via the classifier 2238.

Advantageously, the dimensionality of the data being transmitted fromthe patch to the server for a system that (1) encodes the data via aneural network and (2) compresses the encoded data can be smaller than asystem with (1) just a neural network encoder (without the losslesscompression). Moreover, using lossless compression enables more data tobe transmitted to the remote server 2228 while still maintaining theintegrity of the data itself because the data can be reconstructed viathe lossless decompressor. To illustrate in an example, if there are aplot of 0 values very close to each other, the system can send arepresentative indication of the plot of 0 values instead of having totransmit all 0 values separately. Advantageously, the data that is beingtransmitted from the patch to the server can use less networkthroughput, and can work under tighter network constraints withoutsacrificing accuracy and performance of the neural network. Moreover,such compression can be another form of encryption that can furtherenhances the privacy and security of the data as the data travelsthrough various networks from the patch 2226 to the remote server 2228.

As noted herein, some embodiments of the present disclosure train theneural network to compress data, such as via the ECG encoder 2230,through a first set of layers in a neural network in order to reduce thedimensionality of the output. The first set of layers of the neuralnetwork is trained to output a certain dimensionality. The compresseddata is transmitted to a remote device that applies the output of thefirst set of layers to the second set of layers of the neural network,which can include the steps described herein, such as decoding the dataor determining physiological characteristics of the data.

In traditional neural networks, systems use large numberrepresentations, such as a floating point number (e.g., 32 bits ofdata). A floating point number can represent a very large or very smallvalue, with great precision to many decimal places. These traditionalsystems use such large number representations for very accurate trainingof the neural network and processing of data. However, using such largenumber representations result in inefficiencies in storing, processing,and transmitting the data.

Some embodiments in the present disclosure mitigate such drawbacks byquantizing data. FIG. 26 illustrates a quantizer 2602 to performquantization on the output data of the ECG Encoder 2230 after processingthe first subset of layers 2222 in the neural network. The patch 2226can receive ECG data 2232 of a user. The monitor (e.g., the patch 2226)can encode the data through the first set of layers 2222 of the neuralnetwork (e.g., via the ECG Encoder 2230) and transmit the encoded outputto a quantizer 2602. The quantizer 2602 can quantize the data (e.g.,truncate or round the data) to be transmitted to a remote server 2228.The remote server 2228 can then process the quantized data through thesecond subset of layers 2224 of the neural network.

Such quantization can be optimized to have better lossless compressionperformance while balancing the overall performance of the neuralnetwork. The system can quantize the output of the first set of layersof the neural network before transmitting the data to the externalserver. In some embodiments, quantizing the data can include rounding(e.g., rounding to the nearest integer), truncating, or reducing thenumber of bits for the data representation.

In some embodiments, the amount of quantization can be determined tooptimize the balance between efficiencies and accuracy. A moreaggressive quantization (e.g., truncating more bits from the data) canreduce the size of the data representation to a lower number of bits,but can negatively affect the accuracy of the neural networkpredictions. However, a less aggressive quantization (e.g., truncatingless bits from the data) can increase accuracy, but at the cost ofincreased storage and transmission requirements of the monitor device onthe person, increased storage requirements by having to store largerdata representations at both the patch 2226 and the remote server 2228,and increased network transfer of data between the patch 2226 and theremote server 2228.

A more aggressive quantization can introduce error into the quantizeddata, which can lead to degradation in performance of algorithms thatuse quantization, as noted herein. While a neural network can work to acertain degree in light of the error introduced by quantization, therewill be a point where the error will significantly degrade thealgorithm's performance. Thus, the amount of quantization can beoptimized based on degradation in hardware efficiencies and accuracy ofthe neural network in its classifications via the classifier 2238.

In some embodiments, the neural network is trained to allow for moreaggressive quantization schemes. For example, the neural network can betrained to maximize lossless compression performance. The neural networkcan be trained by introducing quantization during training in order toreduce degradation associated with the quantization. In someembodiments, the neural network is initially trained without thequantization. Then, the trained neural network is trained again withquantization. Advantageously, the training of the neural network takesinto account the quantization. This is particularly relevant here wherethe neural network is divided into a first set of layers that run on thepatch 2226 and a second set of layers that run on a remote server 2228.Advantageously, the results of such training can include a neuralnetwork and/or quantization that can better represent low amplitudesignals from the quantization. Rather the neural network can adjust thevalues of its output (e.g., increasing the amplitude of the lowamplitude signals) to ensure that the low amplitude signals are takeninto consideration, such as by the classifier 2236 of the remote server2228, to identify features in the signal. For example, the neuralnetwork can exaggerate the low amplitude signals such that the signalsreach a threshold. Thus, even as the low amplitude signals are processedby the quantization, the remote server 2228 can still register the lowamplitude signals. Other systems without such training of the neuralnetwork could simply clip the low amplitude signals via thequantization, and the classifier 2238 may not be able to identifyfeatures in such signals.

In some embodiments, both lossless compression and quantization can beoptimized and applied. In some embodiments, the optimal losslesscompression in lieu of the neural network and the quantization can bedetermined by modeling performance of different lossless compressionalgorithms or characteristics thereof. For example, the output offeatures of the neural network can be set to have a distributioncentered at 0 with a standard deviation of 1. Then, the system can modelhow well certain lossless compression algorithms perform under variousquantization schemes. For example, if the output of the first subset oflayers of a neural network is 128 bits per second, the quantizer canquantize the data to output 100 bits per second. The optimization can begoverned at least by the available network and/or network constraints(such as a set number of bits per second or a bit size for the data).Then, lossless compression models (e.g., as described herein) can beapplied to find optimal performance on identified features of the neuralnetwork at the remote server.

In some embodiments, the optimization can be governed at least by thearchitecture of the encoder. For example, a first encoder design can beoutputting 8 bits and a second encoder design can be outputting 4 bitsbut outputting at double the sample rate as the first encoder design. Inthis example, the network constraints can be the same for the first andsecond encoder. The first encoder design has a less aggressivequantization with fewer outputted features. However in somecircumstances, the second encoder design can perform better because ofthe higher rate of feature generation, in spite of the higherquantization.

In some embodiments, the implementation of quantization into trainingcan apply a tensorflow implementation. During training, the tensorflowcan introduce the quantization in the forward pass, but the gradientswill be computed without the quantization. This is done so thatquantization errors are propagated through the network, but gradientsare still smooth and will allow the weight updates to respondappropriately to the errors.

FIG. 27 illustrates an embodiment with both the quantizer 2602 and thelossless compression via the lossless compressor 2502 and the losslessdecompressor 2504. In this embodiment, the output of the ECG Encoder2230, after the encoder has passed the data through the first subset oflayers 2222 of the neural network, is inputted into the quantizer 2602.The quantizer 2602 quantizes the encoded output data. Then, the losslesscompressor 2502 can compress the quantized data to be transmitted 2234to the remote server 2228. The remote server 2228 can then decompressthe transmitted data 2234 via a lossless decompressor 2504, and theclassifier 2238 can process the decompressed data through the secondsubset of layers 2224 of the neural network.

Analysis of QT Interval Algorithms

In some embodiments of the present disclosure, the system can quantifyQT intervals, which can include quantifying the time between the Q and Tmorphological features presented in an individual heartbeat. Anelongated time between these features can be indicative of a heartcondition.

In traditional systems, quantifying the QT interval is typicallyaccomplished by first identifying the different morphological featuresand simply measuring the difference between specific points on thefeatures. However, systems that do not have access to the full raw ECGdata may not be able to apply the traditional methods of quantifying theQT interval.

Described herein are some embodiments that may quantify QT intervalswithout having access to the full raw ECG signal. In some embodiments,the remote server can receive the encoded features from the encoder, andcan reconstruct the ECG signal from the encoded features. Then, theremote server can use signal processing and algorithm techniques forlocating morphological features.

In some embodiments, the remote server can train a machine learningalgorithm to directly predict the QT interval from a segment of encodedfeatures that contain a QT interval.

In some embodiments, the remote server can train a machine learningalgorithm that directly predicts the average QT interval from a windowof encoded features that contain one or more QT intervals.

In some embodiments, the system can generate a template beat that isrepresentative of several beats. The template beat can be generated byaveraging the raw ECG of a fixed window of samples surrounding adetected beat location. The QT interval can be derived from the templatebeat using the algorithm techniques or machine learning algorithmsdescribed herein. FIGS. 28A and 28B illustrate the process forgenerating a template beat. For example, FIG. 28A illustrates a windowof samples over 4 cycles. Each cycle has a fixed window of samples. Thesystem can determine the number of cycles over a time period or set anumber of cycles to be used in the generation of the template beat. FIG.28B illustrates the generation of the template beat from 4 cycles. Insome embodiments, the system determines samples relative to each other.For example, the system can identify corresponding points in each cycleand average each of the samples together. Thus, the 4 cycles illustratedon the left of FIG. 28B can be averaged to generate a template beat onthe right.

In some embodiments, the patch can generate the template beat. In otherembodiments, the remote server can generate the template beat from thereceived data from the patch. In some embodiments, the template beat canbe processed and identified via data processing through the layers ofthe neural network. For example, the patch can generate the templatebeat and input the template beat into the neural network describedherein.

In general, the word “module,” as used herein, refers to logic embodiedin hardware or firmware, or to a collection of software instructions,possibly having entry and exit points, written in a programminglanguage, such as, for example, Python, Java, Lua, C and/or C++. Asoftware module may be compiled and linked into an executable program,installed in a dynamic link library, or may be written in an interpretedprogramming language such as, for example, BASIC, Perl, or Python. Itwill be appreciated that software modules may be callable from othermodules or from themselves, and/or may be invoked in response todetected events or interrupts. Software modules configured for executionon computing devices may be provided on a computer readable medium, suchas a compact disc, digital video disc, flash drive, or any othertangible medium. Such software code may be stored, partially or fully,on a memory device of the executing computing device, such as thecomputing system 13000, for execution by the computing device. Softwareinstructions may be embedded in firmware, such as an EPROM. It will befurther appreciated that hardware modules may be comprised of connectedlogic units, such as gates and flip-flops, and/or may be comprised ofprogrammable units, such as programmable gate arrays or processors. Theblock diagrams disclosed herein may be implemented as modules. Themodules described herein may be implemented as software modules, but maybe represented in hardware or firmware. Generally, the modules describedherein refer to logical modules that may be combined with other modulesor divided into sub-modules despite their physical organization orstorage.

Each of the processes, methods, and algorithms described in thepreceding sections may be embodied in, and fully or partially automatedby, code modules executed by one or more computer systems or computerprocessors comprising computer hardware. The code modules may be storedon any type of non-transitory computer-readable medium or computerstorage device, such as hard drives, solid state memory, optical disc,and/or the like. The systems and modules may also be transmitted asgenerated data signals (for example, as part of a carrier wave or otheranalog or digital propagated signal) on a variety of computer-readabletransmission mediums, including wireless-based and wired/cable-basedmediums, and may take a variety of forms (for example, as part of asingle or multiplexed analog signal, or as multiple discrete digitalpackets or frames). The processes and algorithms may be implementedpartially or wholly in application-specific circuitry. The results ofthe disclosed processes and process steps may be stored, persistently orotherwise, in any type of non-transitory computer storage such as, forexample, volatile or non-volatile storage.

The various features and processes described above may be usedindependently of one another, or may be combined in various ways. Allpossible combinations and subcombinations are intended to fall withinthe scope of this disclosure. In addition, certain method or processblocks may be omitted in some implementations. The methods and processesdescribed herein are also not limited to any particular sequence, andthe blocks or states relating thereto can be performed in othersequences that are appropriate. For example, described blocks or statesmay be performed in an order other than that specifically disclosed, ormultiple blocks or states may be combined in a single block or state.The example blocks or states may be performed in serial, in parallel, orin some other manner. Blocks or states may be added to or removed fromthe disclosed example embodiments. The example systems and componentsdescribed herein may be configured differently than described. Forexample, elements may be added to, removed from, or rearranged comparedto the disclosed example embodiments.

Conditional language, such as, among others, “can,” “could,” “might,” or“may,” unless specifically stated otherwise, or otherwise understoodwithin the context as used, is generally intended to convey that certainembodiments include, while other embodiments do not include, certainfeatures, elements and/or steps. Thus, such conditional language is notgenerally intended to imply that features, elements and/or steps are inany way required for one or more embodiments or that one or moreembodiments necessarily include logic for deciding, with or without userinput or prompting, whether these features, elements and/or steps areincluded or are to be performed in any particular embodiment. The term“including” means “included but not limited to.” The term “or” means“and/or.”

Any process descriptions, elements, or blocks in the flow or blockdiagrams described herein and/or depicted in the attached figures shouldbe understood as potentially representing modules, segments, or portionsof code which include one or more executable instructions forimplementing specific logical functions or steps in the process.Alternate implementations are included within the scope of theembodiments described herein in which elements or functions may bedeleted, executed out of order from that shown or discussed, includingsubstantially concurrently or in reverse order, depending on thefunctionality involved, as would be understood by those skilled in theart.

All of the methods and processes described above may be at leastpartially embodied in, and partially or fully automated via, softwarecode modules executed by one or more computers. For example, the methodsdescribed herein may be performed by the computing system and/or anyother suitable computing device. The methods may be executed on thecomputing devices in response to execution of software instructions orother executable code read from a tangible computer readable medium. Atangible computer readable medium is a data storage device that canstore data that is readable by a computer system. Examples of computerreadable mediums include read-only memory, random-access memory, othervolatile or non-volatile memory devices, CD-ROMs, magnetic tape, flashdrives, and optical data storage devices.

It should be emphasized that many variations and modifications may bemade to the above-described embodiments, the elements of which are to beunderstood as being among other acceptable examples. All suchmodifications and variations are intended to be included herein withinthe scope of this disclosure. The foregoing description details certainembodiments. It will be appreciated, however, that no matter howdetailed the foregoing appears in text, the systems and methods can bepracticed in many ways. For example, a feature of one embodiment may beused with a feature in a different embodiment. As is also stated above,it should be noted that the use of particular terminology whendescribing certain features or aspects of the systems and methods shouldnot be taken to imply that the terminology is being re-defined herein tobe restricted to including any specific characteristics of the featuresor aspects of the systems and methods with which that terminology isassociated.

Various embodiments of a physiological monitoring device, methods, andsystems are disclosed herein. These various embodiments may be usedalone or in combination, and various changes to individual features ofthe embodiments may be altered, without departing from the scope of theinvention. For example, the order of various method steps may in someinstances be changed, and/or one or more optional features may be addedto or eliminated from a described device. Therefore, the description ofthe embodiments provided above should not be interpreted as undulylimiting the scope of the invention as it is set forth in the claims.

Various modifications to the implementations described in thisdisclosure may be made, and the generic principles defined herein may beapplied to other implementations without departing from the spirit orscope of this disclosure. Thus, the scope of the disclosure is notintended to be limited to the implementations shown herein, but are tobe accorded the widest scope consistent with this disclosure, theprinciples and the novel features disclosed herein.

Certain features that are described in this specification in the contextof separate embodiments also can be implemented in combination in asingle embodiment. Conversely, various features that are described inthe context of a single embodiment also can be implemented in multipleembodiments separately or in any suitable subcombination. Moreover,although features may be described above as acting in certaincombinations and even initially claimed as such, one or more featuresfrom a claimed combination can in some cases be excised from thecombination, and the claimed combination may be directed to asubcombination or variation of a subcombination.

Similarly, while operations are depicted in the drawings in a particularorder, such operations need not be performed in the particular ordershown or in sequential order, or that all illustrated operations beperformed, to achieve desirable results. Further, the drawings mayschematically depict one more example processes in the form of a flowdiagram. However, other operations that are not depicted can beincorporated in the example processes that are schematicallyillustrated. For example, one or more additional operations can beperformed before, after, simultaneously, or between any of theillustrated operations. Moreover, the separation of various systemcomponents in the embodiments described above should not be interpretedas requiring such separation in all embodiments. Additionally, otherembodiments are within the scope of the following claims. In some cases,the actions recited in the claims can be performed in a different orderand still achieve desirable results.

What is claimed is:
 1. A monitor comprising: a sensor positioned todetect continuous physiological signals of a user while the monitor isengaged to the user; and a hardware processor configured to process thedetected physiological signals through an encoder to generate dataoutput, wherein the encoder comprises a first subset of layers of aneural network; wherein an external computing system is configured todetermine or infer a cardiac event of the user by processing the dataoutput or a signal derived from the data output through a decoder,wherein the decoder comprises a second subset of layers of the neuralnetwork.
 2. The monitor of claim 1, wherein the cardiac event comprisesa likelihood of an occurrence of arrhythmia in the past of when thesignals were detected.
 3. The monitor of claim 1, wherein the cardiacevent comprises a likelihood of an occurrence of arrhythmia in thefuture of when the signals were detected.
 4. The monitor of claim 1,wherein the cardiac event comprises at least one of: a heartabnormality, a heart failure, a prognostic prediction, sleep apnea,hypertension, or blood pressure.
 5. The monitor of claim 1, wherein thecardiac event comprises at least one of: a condition only onelectrocardiography (ECG) data.
 6. The monitor of claim 1, wherein thecardiac event comprises at least one of: a condition only onphotoplethysmography (PPG) data.
 7. The monitor of claim 1, wherein themonitor includes a transmitter configured to transmit the data output ofthe encoder to the external computing system.
 8. The monitor of claim 1,wherein the monitor is a wearable patch.
 9. The monitor of claim 1,wherein the monitor is a patch that is applied on the chest of the user.10. The monitor of claim 1, wherein the monitor is an implantabledevice.
 11. The monitor of claim 1, wherein the monitor comprisesanother sensor configured to detect accelerometer configured to gathermotion data, wherein the external computing system is configured tomatch motion data with the detected physiological signals to determineor infer the cardiac event.
 12. The monitor of claim 1, wherein thefirst subset of layers of the neural network and the second subset oflayers of the neural network are trained together.
 13. A monitorcomprising: a sensor positioned to detect physiological signals of auser while the monitor is engaged to the user; and a hardware processorconfigured to process the detected physiological signals through a firstsubset of layers of a neural network; wherein an external computingsystem is configured to determine or infer a physiologicalcharacteristic of the user by processing data output or a signal derivedfrom the data output through a second subset of layers of the neuralnetwork.
 14. The monitor of claim 13, wherein the dimensionality of thedata output of the first subset of layers of the neural network issmaller than the data of the detected physiological signals from thesensor.
 15. The monitor of claim 13, wherein the data output of thefirst subset of layers of the neural network is encrypted, wherein theexternal computing system processes the encrypted data output throughthe second subset of layers of the neural network.
 16. The monitor ofclaim 13, further comprising a receiver configured to receive an updatedfirst subset of layers of the neural network from the external computingsystem and updating the first subset of the layers of the neural networkto the updated first subset of layers of the neural network, wherein thehardware processor is further configured to process signals through theupdated first subset of layers of the neural network.
 17. The monitor ofclaim 13, wherein the monitor includes a cardiac monitor, and whereinthe continuous physiological signals are cardiac signals.
 18. Themonitor of claim 13, wherein to infer a likelihood of an occurrence ofcardiac arrhythmia comprises processing the data output of the firstsubset through the second subset, wherein the first subset processes atleast 24 hours of continuously detected, stored physiological signals.19. The monitor of claim 13, wherein the monitor further comprises apatient trigger configured to depress and initiate recordation of aninstance in time of a perceived cardiac event.
 20. A monitor comprising:a sensor configured to detect signals of a patient when engaged with abody of the patient; and a signal processor configured to processdetected patient signals through at least a first portion of a machinelearning network to generate a first output, wherein the signalprocessor is local to the monitor; wherein a computing system isconfigured to process the first output or a signal derived from thefirst output through at least a second portion of the machine learningnetwork, wherein the computing system is external to the monitor. 21.The monitor of claim 20, wherein the signal processor is configured toselect the machine learning network from a plurality of machine learningnetwork based on a characteristic of the monitor.
 22. The monitor ofclaim 21, wherein the characteristic of the monitor comprises one ormore of: a remaining amount of battery, a network characteristic betweenthe monitor and the computing system, or a wear duration.
 23. Themonitor of claim 21, wherein the characteristic includes one or more of:a characteristic of the patient or a severity of cardiac arrhythmia. 24.The monitor of claim 20, wherein the signal processor is furtherconfigured to compress the first output, wherein the computing system isconfigured to decompress the compressed data; and wherein processing thefirst output through the second portion comprises processing thedecompressed data.
 25. The monitor of claim 20, wherein the signalprocessor is further configured to quantize the first output of thefirst portion.
 26. The monitor of claim 25, wherein quantizing comprisesrounding, truncating, or reducing a number of bits for the data outputof the first portion.
 27. The monitor of claim 25, wherein the signalprocessor is further configured to determine an amount of quantizationbased on one or more of: a characteristic of the monitor or a losslesscompression performance.
 28. The monitor of claim 25, wherein the signalprocessor is further configured to determine an amount of quantizationbased on at least one of: a processing power, a storage capacity, anamount of remaining storage capacity, or a network characteristic. 29.The monitor of claim 25, wherein the signal processor is furtherconfigured to determine an amount of quantization based on an accuracyof the machine learning network.